Given that LES provides a set of specific operations O and x is a variable over this set to fulfil a t that is a variable over the set of tasks T, it would be required to have a subset S' from the skill set of S.

$$[LES(y) \to (provisids(y, x) \land (O \lor S))]\tag{6}$$

We must ensure that every LES carries enough semantic information to facilitate the runtime reconfiguration in the case of a web service failure during the LEI execution. Every LES has an object property 'hasPrerequisite' that makes one LES the prerequisite service of the value of this property in the workflow of LESs. Each of these object properties points to another LES, essentially creating a linked list of LESs in which every LES is aware of its place in the list through the data property called WorkflowPosition. In other words, a life event is the construct of a two-dimensional linked list, in which the first dimension is the list of meta-services and the second dimension is the list of service instances for each meta-service.

#### 6. Life-event ontology evaluation

Life-event candidate is an abstraction of a complex workflow consisting of a number of composite or simple web services. Life-event candidate can use OWL-S descriptors of web services provided by service provider including the required government regulatory information, which

In the following, we provide a formal description for the main concepts (classes) of our life-

We shall use the predicate 'concept' in order to denote concepts, thus 'concept(x)' is to be read, 'x is a concept', and a concept can be from any of the following types: (LE, LEI, LES, SI, PR, ST

Symbolically, the first axiom is an existential one, which asserts that there is at least one entity of a certain type. Here, we indicate that there exists ontology ω and there exists entity x that is

We use the predicate Ω in order to denote token, thus 'Ω(ω)' is to be read 'ω is an ontology'. An instance of a given ontology token α is an entity whose existence is recognised by α. We will write 'inst(x, α)' which is to be read 'x is an instance of α'. Hence, there is no empty life-event

The predicate 'realises' denotes the instances of associated LES concepts within the life-event ontology. While each instance may be a model on its own, a combination of LESs may be aggregated to constitute a composite model. In that case, the services are considered to be the components of a model. LES (y) play role (r) that requires skills (s) needed to perform

∃ω, ∃x½ � ΩðωÞ ∧ LEðxÞ (2)

instðx, ωÞ ! ½ � conseptðxÞ ∧ ΩðωÞ (3)

ΩðωÞ ! ∃x consept ½ � ðxÞ ∧ instðx, ωÞ (4)

and SST). We will use x, y and z as variables ranging over concepts.

of concept (class) life event in a variable ontology ω.

In addition, any existence is a constituent of ontology.

is required to guarantee a legally acceptable outcome whenever the service is executed.

event ontology followed by their description logic:

Life-event ¼ LE

118 Ontology in Information Science

Life-eventInstance ¼ LEI Life-eventService ¼ LES

ServiceInstance ¼ SI

ServiceSubType ¼ SST

Prerequisite ¼ PR ServiceType ¼ ST

ontology.

their role.

There is no restriction on the complexity of the logic that may be used to state the axioms and definitions of concepts in ontology. The distinction between terminological and formal ontologies is one of degree rather than kind. Life-event ontology tends to be smaller than terminological ontologies, but its axioms and definitions can support more complex inferences and computations. We conduct the experimental evaluation of the life-event ontology in two stages. We use the ontology editor tool Protégé to design and develop the life-event ontology schema as the preparation for evaluating the life-event ontology. We use the FaCTþþ reasoner plug-in from within Protégé to perform structural validation of the schema. To evaluate the efficiency of life-event ontology, an experiment is conducted to measure the complexity of the ontology through a set of well-known formal methods and demonstrate the results in a numerical as well as graphical representation. We compare the life-event ontology to OWL-S ontology since it is the most conceptually similar to it.

#### 6.1. Methods of measuring the ontology complexity

As ontologies grow in size and number, it is important to be able to measure their complexity quantitatively. Quantitative measurement of complexity can help ontology developers and maintainers better understand the current status of the ontology, therefore allowing them to better evaluate its design and control its development process. We are using a suite of ontology metrics [11], at both the ontology level and the class level, to measure the design complexity of life-event ontology. This ontology complexity measurement metric was evaluated in an empirical analysis on public domain ontologies to show the characteristics and usefulness of the metrics. The proposed metric suite is useful for managing the life-event ontology development projects.

#### 6.2. Ontology level metrics

We use three different ontology level metrics to measure the complexity of the ontology:

Size of vocabulary (SOV) measures the amount of vocabulary defined in ontology. Given a graph representation G ¼ (N, P, E) of an ontology, where N is a set of nodes representing classes and individuals; P is a set of nodes representing properties and E is a set of edges representing property instances and other relationships between nodes in the graph G. In this measurement, SOV is defined as the cardinality of the named entities Nn and Pn in G: SOV ¼ jNnjþjPnj, where Nn represents named classes and individuals and Pn represents user-defined properties.

Edge node ratio (ENR) measures the connectivity density. In this measurement, ENR tends to increases as more edges are added between nodes. The greater the ENR, the greater the complexity of an ontology. ENR is calculated as follows:

$$ENR = \frac{|E|}{|N|},\tag{7}$$

as the division of the number of edges ðjEjÞ by the number of nodes ðjNjÞ.

Tree impurity(TIP) measures how far ontology's inheritance hierarchy deviates from being a tree and it is defined as being:

$$TIP = |E'| - |N'| + 1,\tag{8}$$

Stanford University. We used version 4.1, which was more advanced, intuitive and easier to use than other available ontology editors. Figure 2 shows a screenshot of our developed ontology, which illustrates the concepts, their relationships with object properties and data

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Step 2: Comparable ontology. This step is to select an ontology that is conceptually similar to life-event ontology. OWL-S is chosen because it is not only conceptually very similar to the lifeevent ontology but also functionally designed to perform a similar work. This ontology is also used by the LOOSI platform to provide knowledgebase support for the web service enactment functionality of the system. The diagram shown in Figure 3 is the graph representation of

In this comparative evaluation of life-event ontology with OWL-S, we use numerical value results from applying the metrics in Sections 6.2.2 and 8.2.3 on both ontologies to illustrate the measurement of efficiency and complexity of the life-event ontology in compare to the OWL-S. The experiment starts with creating and adding five named individuals that are the representatives of five individual web services that are published by the Australian Government agencies and other businesses. One more named individual is created only in life-event ontology as the first instance of the schema to point to the Life-eventServices. The list of these

Considering the populated OWL-S ontology and the life-event ontology, we use the actual measurements with the methods described in Sections 6.2.2 and 8.2.3 and calculate the results

properties in the Protégé ontology editor.

Figure 2. Life-event ontology built by Protégé.

OWL-S conceptual schema version 1.1.

named individuals is described in Figure 4.

as follows:

where jE0 j is the number of subclass edges and jN0 j is the number of nodes in an ontology's inheritance hierarchy.

#### 6.3. Class level metrics

This metrics are mostly concerned with the class level specific statistics, the most popular technique in this method is known as number of children (NOC), as such to calculate NOC for a given class C, NOC measures the number of its immediate children in the ontology inheritance hierarchy given, as follows:

$$\text{NOC}\_{\text{c}} = \# \{ D | D \in \text{N}' \land (D, rdfs : sub\text{ClassO}f, \text{C}) \in \text{E}' \}, \tag{9}$$

where C∈ N0 and symbol # denote the cardinality. And, the E0 denotes the set of entities.

#### 6.4. Experiment preparation

This section will describe the preparation for a comparative evaluation of life-event ontology against OWL-S using the methods described in Sections 6.2.2 and 6.2.3.

Step 1: Building the ontology. It aims to prepare for the evaluation of the life-event ontology. The choice of ontology editor was made mainly due to the fact that Protégé is open source software and was more suited to our purpose [14]. This tool is developed and maintained by

Figure 2. Life-event ontology built by Protégé.

6.2. Ontology level metrics

120 Ontology in Information Science

user-defined properties.

tree and it is defined as being:

where jE0

inheritance hierarchy.

6.3. Class level metrics

tance hierarchy given, as follows:

6.4. Experiment preparation

complexity of an ontology. ENR is calculated as follows:

j is the number of subclass edges and jN0

NOCc ¼ # DjD ∈ N0

against OWL-S using the methods described in Sections 6.2.2 and 6.2.3.

We use three different ontology level metrics to measure the complexity of the ontology:

Size of vocabulary (SOV) measures the amount of vocabulary defined in ontology. Given a graph representation G ¼ (N, P, E) of an ontology, where N is a set of nodes representing classes and individuals; P is a set of nodes representing properties and E is a set of edges representing property instances and other relationships between nodes in the graph G. In this measurement, SOV is defined as the cardinality of the named entities Nn and Pn in G: SOV ¼ jNnjþjPnj, where Nn represents named classes and individuals and Pn represents

Edge node ratio (ENR) measures the connectivity density. In this measurement, ENR tends to increases as more edges are added between nodes. The greater the ENR, the greater the

> ENR ¼ jEj jNj

Tree impurity(TIP) measures how far ontology's inheritance hierarchy deviates from being a

This metrics are mostly concerned with the class level specific statistics, the most popular technique in this method is known as number of children (NOC), as such to calculate NOC for a given class C, NOC measures the number of its immediate children in the ontology inheri-

where C∈ N0 and symbol # denote the cardinality. And, the E0 denotes the set of entities.

This section will describe the preparation for a comparative evaluation of life-event ontology

Step 1: Building the ontology. It aims to prepare for the evaluation of the life-event ontology. The choice of ontology editor was made mainly due to the fact that Protégé is open source software and was more suited to our purpose [14]. This tool is developed and maintained by

j�jN0

as the division of the number of edges ðjEjÞ by the number of nodes ðjNjÞ.

TIP ¼ jE0

, (7)

j þ 1, (8)

j is the number of nodes in an ontology's

∧ ðD, rdf s : subClassOf , CÞ∈E0 , (9)

Stanford University. We used version 4.1, which was more advanced, intuitive and easier to use than other available ontology editors. Figure 2 shows a screenshot of our developed ontology, which illustrates the concepts, their relationships with object properties and data properties in the Protégé ontology editor.

Step 2: Comparable ontology. This step is to select an ontology that is conceptually similar to life-event ontology. OWL-S is chosen because it is not only conceptually very similar to the lifeevent ontology but also functionally designed to perform a similar work. This ontology is also used by the LOOSI platform to provide knowledgebase support for the web service enactment functionality of the system. The diagram shown in Figure 3 is the graph representation of OWL-S conceptual schema version 1.1.

In this comparative evaluation of life-event ontology with OWL-S, we use numerical value results from applying the metrics in Sections 6.2.2 and 8.2.3 on both ontologies to illustrate the measurement of efficiency and complexity of the life-event ontology in compare to the OWL-S.

The experiment starts with creating and adding five named individuals that are the representatives of five individual web services that are published by the Australian Government agencies and other businesses. One more named individual is created only in life-event ontology as the first instance of the schema to point to the Life-eventServices. The list of these named individuals is described in Figure 4.

Considering the populated OWL-S ontology and the life-event ontology, we use the actual measurements with the methods described in Sections 6.2.2 and 8.2.3 and calculate the results as follows:

Figure 3. OWL-S ontology schema [10].

Based on the SOV method, we measure the SOV value of the life event to be 7 þ 8 ¼ 15, and for OWL-S to be 12 þ 9 ¼ 21, this means that if we consider the growth ratio for the life event as being the base 15/15 ¼ 1 then this for the OWL-S would be 21/15 ¼ 1.4. Table 1 details the numerical representation of the growth of ontology data in both ontologies.

We register the statistics in Table 1 by assuming the initial SOV as to be the sum of the nodes, plus object properties in the ontology schema. Then we increased this number five times as per the number of named individuals representing the web services created for this experiment, each time by the amount of SOV ratio, representing the linear growth in the volume of ontology data.

Figure 5 shows the comparative graph representation that illustrates the trend of growth in the life-event ontology data and the OWL-S ontology data. It is shown that the rate of growth in the volume of data in the life event is dramatically less than the OWL-S, after a fivefold increases in the number of named individuals.

a. Based on the ENR method, we measure the ENR value of both ontologies in question to be as follows:

$$\text{Life\\_-event} = \frac{|15|}{|7|} = 2.5, \text{\textbullet{OWL}\\_-S} = \frac{|21|}{|8|} = 2.63. \tag{10}$$

Figure 4. Life event and OWL-S named individuals.

Life event OWL-S 15 21 30 36 60 86.4 120 207.36 240 497.7

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Table 1. Numerical representation of ontology growth as per SOV ratio.

Table 2 shows the growth of ontology data in both ontologies in terms of ENR in numerical terms. The statistics shown in Table 2 is obtained by initial ENR 1 is increased five times as per the number of web service named individuals, created for this experiment, each time by the amount of ENR ratio.

Based on the SOV method, we measure the SOV value of the life event to be 7 þ 8 ¼ 15, and for OWL-S to be 12 þ 9 ¼ 21, this means that if we consider the growth ratio for the life event as being the base 15/15 ¼ 1 then this for the OWL-S would be 21/15 ¼ 1.4. Table 1 details the

We register the statistics in Table 1 by assuming the initial SOV as to be the sum of the nodes, plus object properties in the ontology schema. Then we increased this number five times as per the number of named individuals representing the web services created for this experiment, each time by the amount of SOV ratio, representing the linear growth in the volume of

Figure 5 shows the comparative graph representation that illustrates the trend of growth in the life-event ontology data and the OWL-S ontology data. It is shown that the rate of growth in the volume of data in the life event is dramatically less than the OWL-S, after a fivefold

a. Based on the ENR method, we measure the ENR value of both ontologies in question to be

j7j ¼ 2:5, OWL �S ¼ j21j

Table 2 shows the growth of ontology data in both ontologies in terms of ENR in numerical terms. The statistics shown in Table 2 is obtained by initial ENR 1 is increased five times as per the number of web service named individuals, created for this experiment,

j8j ¼ 2:63: (10)

numerical representation of the growth of ontology data in both ontologies.

ontology data.

Figure 3. OWL-S ontology schema [10].

122 Ontology in Information Science

as follows:

increases in the number of named individuals.

each time by the amount of ENR ratio.

Life �event ¼ j15j

Table 1. Numerical representation of ontology growth as per SOV ratio.

Figure 6 shows the comparative graph illustrating the trend of growth in ENR for the lifeevent ontology and OWL-S ontology in the event of growth in ontology data. It is shown that this increase in life-event ontology is less than OWL-S after a few fold increases in the

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b. Based on the TIP method, we measure the value of 'how far life event deviates from being a tree?' to be (15 � 7 þ 1 ¼ 9), and for OWL-S to be (21 � 8 þ 1 ¼ 14). As it is shown that

c. Using the NOC method, we calculated the number of immediate children (rdf: subClassOf) for the class Parameter that is the most frequently used in web service invocation to be 3. The value of NOC calculated for Life-eventService, which is the most used class in lifeevent ontology, is 2. Table 3 shows the complexity growth of ontology data for class parameter in OWL-S in comparison with the class Life-eventService in life-event ontology in terms of the NOC ratio. The statistics shown in Table 3 is obtained by initial NOC 1 increased five times as per the number of web service named individuals, created for this

this value is greater for OWL-S than for life-event ontology.

experiment, each time by the amount of NOC ratio.

Life event OWL-S 2 3 4 9 8 27 16 81 32 243

Table 3. Numerical representation of physical growth for ontology classes as per NOC ratio.

Figure 7. Physical representation of growths in complexity of ontology data as per NOC ratio.

number of named individuals.

Figure 5. Physical representation of growths in ontology data as per SOV ratio.

Table 2. Numerical representation of physical growth for ontology as per ENR ratio.

Figure 6. Physical representation of growths in ontology data per ENR ratio.

Figure 6 shows the comparative graph illustrating the trend of growth in ENR for the lifeevent ontology and OWL-S ontology in the event of growth in ontology data. It is shown that this increase in life-event ontology is less than OWL-S after a few fold increases in the number of named individuals.

Table 3. Numerical representation of physical growth for ontology classes as per NOC ratio.

Figure 5. Physical representation of growths in ontology data as per SOV ratio.

124 Ontology in Information Science

Life event OWL-S 2.5 2.63 6.25 6.9 15.63 18.2 39.1 47.8 97.75 125.8

Table 2. Numerical representation of physical growth for ontology as per ENR ratio.

Figure 6. Physical representation of growths in ontology data per ENR ratio.

Figure 7. Physical representation of growths in complexity of ontology data as per NOC ratio.

Figure 7 shows the comparative graph illustrating the trend of growth in complexity as per the NOC ratio for class parameter in OWL-S in comparison with the class Life-eventService in lifeevent ontology in the event of growth in ontology data. It is shown that this increase in life-event ontology is less than OWL-S after a few fold increases in the number of named individuals.

[7] McGuinness DL. OWL Web Ontology Language Overview [Internet]. 2004. Available

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[8] Tsarkov D, Horrocks I. FaCTþþ description logic reasoner: System description. In: Proceeding of the International Joint Conference on Automated Reasoning (IJCAR 2006).

[9] Koponen T, Virtanen T. A service discovery:A service broker approach. In: 37th Annual Hawaii Internation Conference on System Science. 2004: Hawaii, USA. pp. 284-290

[11] Zhang H, Li Y-F, Tan HBK. Measuring design complexity of semantic web ontologies.

[12] Ferrario R, Guarino N. Towards an ontological foundation for services science. In: Future

[13] Lytras MD, García R. Semantic web applications: A framework for industry and business exploitation—What is needed for the adoption of the semantic web from the market and industry. International Journal of Knowledge and Learning. 2008 Jan 1;4(1):93-108 [14] Stanford-University. Protégé. 2009. Available from: http://protege.stanford.edu/. [Accessed:

[10] W3C. OWL-S: Semantic Markup for Web Services. 2004 [16 November 2010]

Journal of Systems and Software. 2009;83(5):803-814

Internet Symposium. 2008. Springer

from: http://www.w3.org/TR/2004/REC-owl-features-20040210/

2006. Springer

2010]

## 7. Summary

In this chapter, we outlined our design strategies to extend the OWL-S in order to propose lifeevent ontology as an abstract design and execution unit for composing e-services. We introduced an ontology that accommodates the concept of life event within the process of e-services composition. The idea was to introduce an innovative approach towards the whole process of e-service composition and delivery.

We put forward a formal design for an ontology knowledgebase to manage the workflow of composite web service workflows in a linear approach. Nevertheless, this research also recognises that more research is required to specify and formalise the design of more complex types of web service composition such as parallel service processes in complex workflows.

## Author details

Farzad Sanati

Address all correspondence to<EMAIL_ADDRESS>
The American University of Iraq, Sulaimani, Iraq

## References

Figure 7 shows the comparative graph illustrating the trend of growth in complexity as per the NOC ratio for class parameter in OWL-S in comparison with the class Life-eventService in lifeevent ontology in the event of growth in ontology data. It is shown that this increase in life-event ontology is less than OWL-S after a few fold increases in the number of named individuals.

In this chapter, we outlined our design strategies to extend the OWL-S in order to propose lifeevent ontology as an abstract design and execution unit for composing e-services. We introduced an ontology that accommodates the concept of life event within the process of e-services composition. The idea was to introduce an innovative approach towards the whole process of

We put forward a formal design for an ontology knowledgebase to manage the workflow of composite web service workflows in a linear approach. Nevertheless, this research also recognises that more research is required to specify and formalise the design of more complex types

[1] Sanati, F. and J. Lu, An ontology for e-government service integration. International

[3] Berners-Lee T, Hendler J, Lassila O. The semantic web. Scientific American. 2001;2845:34-43 [4] Maedche A. Ontology Learning for the Semantic Web. Kluwer International Series in Engineering and Computer Science; SECS665. Boston: Kluwer Academic; 2002

[5] Berners-Lee T, Fielding R, Masinter L. Uniform Resource Identifiers (URI): Generic Syn-

[6] Valencia-Garcia R, et al. OWLPath: An owl ontology-guided query editor. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans. 2011;41(1):121-136

Journal of Computer Systems Science And Engineering. 2012;27(2):89-101

[2] Smith MK, Welty C, McGuinness DL. Owl Web Ontology Guide. 2003 [cited 2011]

of web service composition such as parallel service processes in complex workflows.

7. Summary

126 Ontology in Information Science

Author details

Farzad Sanati

References

tax. RFC Editor 1998

e-service composition and delivery.

Address all correspondence to<EMAIL_ADDRESS>
The American University of Iraq, Sulaimani, Iraq

**Chapter 6**

Provisional chapter

**Ontology in IT Projects Based on OSM**

Ontology in IT Projects Based on OSM

Project management is the discipline of initiating, planning, executing, controlling, and closing the work of a team to achieve specific goals and meet specific success criteria. However, there can exist a considerable amount of uncertainty in decision making for project management stemming from the typical complex characteristics of projects. This study focused on two primary objectives: (1) to understand the role of project management in complex situations and (2) to offer a more robust model for project management. Pertinent literature on models for project management was collected and synthesized for application in complex situations. A cybernetic-based model is then developed. This model enables a manager to focus on internal organizational complexity while paying enough attention to external perturbations. The utility of the model is

DOI: 10.5772/intechopen.72729

Keywords: project management, ontology, cybernetic management, complex systems

1. Systemic management models and their link to project management

Today, a project is defined by a defined start and end, constrained by time, funds, and measured by expected deliverables. So, the project organization within an organization takes place within

Aim of projects is to deliver value to customers. The task necessary to deliver value is intricately related to many factors, some of which can be beyond the scope of the project itself as it is related to being a temporary organization within an organization. Enterprises today are socio-technical systems, constantly interacting with an increasingly complex environment. Investments are often done within specific projects as a temporary organization within an organization. The OSTO® System Model (OSM) deals with processes that are strongly interrelated and is a result of a combination of technology, work organization, and human communication. The OSM is robust

> © The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

demonstrated in a case application in an organization in Belgium.

the confines of operations with a bearing on project management.

and mature model used over years in several management reorganizations [1].

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72729

Frank Stumpe

Frank Stumpe

Abstract

Provisional chapter

## **Ontology in IT Projects Based on OSM** Ontology in IT Projects Based on OSM

## Frank Stumpe Frank Stumpe

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72729

## Abstract

Project management is the discipline of initiating, planning, executing, controlling, and closing the work of a team to achieve specific goals and meet specific success criteria. However, there can exist a considerable amount of uncertainty in decision making for project management stemming from the typical complex characteristics of projects. This study focused on two primary objectives: (1) to understand the role of project management in complex situations and (2) to offer a more robust model for project management. Pertinent literature on models for project management was collected and synthesized for application in complex situations. A cybernetic-based model is then developed. This model enables a manager to focus on internal organizational complexity while paying enough attention to external perturbations. The utility of the model is demonstrated in a case application in an organization in Belgium.

DOI: 10.5772/intechopen.72729

Keywords: project management, ontology, cybernetic management, complex systems

## 1. Systemic management models and their link to project management

Today, a project is defined by a defined start and end, constrained by time, funds, and measured by expected deliverables. So, the project organization within an organization takes place within the confines of operations with a bearing on project management.

Aim of projects is to deliver value to customers. The task necessary to deliver value is intricately related to many factors, some of which can be beyond the scope of the project itself as it is related to being a temporary organization within an organization. Enterprises today are socio-technical systems, constantly interacting with an increasingly complex environment. Investments are often done within specific projects as a temporary organization within an organization. The OSTO® System Model (OSM) deals with processes that are strongly interrelated and is a result of a combination of technology, work organization, and human communication. The OSM is robust and mature model used over years in several management reorganizations [1].

© 2018 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

As mentioned above, a project is aimed to delivers outputs as deliverables. In a good design, the output is the customers expected out-come, following the objectives. In the OSM, the objectives are injected into the system as a target to be reached while the system delivers the output. One should deliver what is requested, in the scope, quality, timing, and within budget, for the system to be considered reliable. Thus, one can conclude that an output is not sufficient but necessary. Clearly, other terms need to be considered. These include mission statements involving reason of existence and meaning. Reason for system existence relates to the needs or expectations of customers to make use of the products or services of the business system (Figure 1). It is imperative that system actors know the "reason of existence" of the system. This involves long-term value-creation of the system leading to future-oriented execution of output, long-term thinking, and looking forward at the wider context rather than exclusively considering the economic reality of today [2].

and translate them into actions. As the actor injects signals into the system, the sensor extracts information from the system and its output, and changes this information into a certain language

Ontology in IT Projects Based on OSM http://dx.doi.org/10.5772/intechopen.72729 131

A project as system consists of at least three main elements: objectives, inputs, and outputs. Objectives include a description of a situation beyond project conclusion. In objectives, there is also need to consider constraints that would enable or hinder the project as well as ensuring enough focus to enable delivery of the expected outcomes. Furthermore, objectives might depend on non-linearity of the situation which could be dominated by "the butterfly effect." For a project manager, this implies that managing of a project is not static, since objectives

Inputs describe everything the project gets to execute, including all necessary actions and tasks, to deliver outputs according objectives. As the project is being executed, inputs are expected to change. Therefore, input is not fixed in time but is dynamic. Additionally, and in complex projects, it is expected that resources, associated with inputs, will change during execution. Reasons associated with such changes include, but not limited to capabilities, contractual obligations, and illness to stakeholders. Moreover, the scope could be redefined by the newly discovered insights in design and unforeseen circumstances. Outputs are everything the project produces. This involves physical and theoretical outputs. Physical outputs are the deliverables including documents, software, and equipment. Theoretical outputs include insights, knowledge, and experiences. In this case, projects, like complex systems, will have

The OSM identifies three processes: Target Core Process (TCP), Individual Core Process (ICP), and Organizational Core Process (OCP). TCP combines all activities, communication, and tasks necessary to produce the expected output. This is known as one-dimensional project management. ICP is influenced by the mood of each individual human, which can be influenced by situations within and outside the system. OCP considers interactions among

As previously suggested, there are "hard" and "soft" elements and that these interact in project management. The sensor interprets the output of the system and provides measurements for the management. The sensor also looks on other steering parameters like objectives, reason for existing, reason for meaning and basic reason, as well as their changes. However, each sensor has a limited view of the system by its own boundaries. The actor gets the results of the project managers decisions based on the data of the sensor. These results will be transformed into steering input. Then, the actor injects this into the system in a structured way to steer the project. However, this planning is with uncertainties. Moreover, the system must contend with external disturbances, involving inputs not planned for or foreseen by the

Starting with the abstraction that the probability of each state of a system is 100%, it is possible to model system dynamics and interactions. Any process or change in state of the system is

to allow closing the cybernetic loop toward the actor.

could change based on individual, social, and environmental factors.

interacting components and processes, influencing output.

individuals, system organization, and the project itself [3].

steering. These disturb the efficiency and effectiveness of the system.

2. Model of the system

As previously mentioned, system components interact among themselves with a given system, as well as interacting with the environment. There is the need for stability and orientation within investment management. This orientation can only be given on a vision level through the statement of the system meaning. As suggested by a cybernetic model of a system, long-term survival of the business depends on having defined sustainable and future-oriented meaning statements. The focus here is on the system usefulness in terms of sustainability involving context of individual, cultural, ethical questions, and expectations within society. Such meaning statements imply that there are values defined for the owner of the investment. In this case, values are one-dimensional for measuring effectiveness. Effectiveness is assessed by quality metric, and efficiency is also measured as a productivity metric. Quality of output, in this perspective, is related to products or services especially the value assigned by the customers.

Beyond the original OSM, two new artifacts are introduced in the system model: Actor and Sensor. The actor has the responsibility to understand certain orders (e.g., management decisions)

Figure 1. Complex cybernetic investment management framework based on OSM.

and translate them into actions. As the actor injects signals into the system, the sensor extracts information from the system and its output, and changes this information into a certain language to allow closing the cybernetic loop toward the actor.

A project as system consists of at least three main elements: objectives, inputs, and outputs. Objectives include a description of a situation beyond project conclusion. In objectives, there is also need to consider constraints that would enable or hinder the project as well as ensuring enough focus to enable delivery of the expected outcomes. Furthermore, objectives might depend on non-linearity of the situation which could be dominated by "the butterfly effect." For a project manager, this implies that managing of a project is not static, since objectives could change based on individual, social, and environmental factors.

Inputs describe everything the project gets to execute, including all necessary actions and tasks, to deliver outputs according objectives. As the project is being executed, inputs are expected to change. Therefore, input is not fixed in time but is dynamic. Additionally, and in complex projects, it is expected that resources, associated with inputs, will change during execution. Reasons associated with such changes include, but not limited to capabilities, contractual obligations, and illness to stakeholders. Moreover, the scope could be redefined by the newly discovered insights in design and unforeseen circumstances. Outputs are everything the project produces. This involves physical and theoretical outputs. Physical outputs are the deliverables including documents, software, and equipment. Theoretical outputs include insights, knowledge, and experiences. In this case, projects, like complex systems, will have interacting components and processes, influencing output.

The OSM identifies three processes: Target Core Process (TCP), Individual Core Process (ICP), and Organizational Core Process (OCP). TCP combines all activities, communication, and tasks necessary to produce the expected output. This is known as one-dimensional project management. ICP is influenced by the mood of each individual human, which can be influenced by situations within and outside the system. OCP considers interactions among individuals, system organization, and the project itself [3].

As previously suggested, there are "hard" and "soft" elements and that these interact in project management. The sensor interprets the output of the system and provides measurements for the management. The sensor also looks on other steering parameters like objectives, reason for existing, reason for meaning and basic reason, as well as their changes. However, each sensor has a limited view of the system by its own boundaries. The actor gets the results of the project managers decisions based on the data of the sensor. These results will be transformed into steering input. Then, the actor injects this into the system in a structured way to steer the project. However, this planning is with uncertainties. Moreover, the system must contend with external disturbances, involving inputs not planned for or foreseen by the steering. These disturb the efficiency and effectiveness of the system.

## 2. Model of the system

As mentioned above, a project is aimed to delivers outputs as deliverables. In a good design, the output is the customers expected out-come, following the objectives. In the OSM, the objectives are injected into the system as a target to be reached while the system delivers the output. One should deliver what is requested, in the scope, quality, timing, and within budget, for the system to be considered reliable. Thus, one can conclude that an output is not sufficient but necessary. Clearly, other terms need to be considered. These include mission statements involving reason of existence and meaning. Reason for system existence relates to the needs or expectations of customers to make use of the products or services of the business system (Figure 1). It is imperative that system actors know the "reason of existence" of the system. This involves long-term value-creation of the system leading to future-oriented execution of output, long-term thinking, and looking forward at the wider context rather than exclusively

As previously mentioned, system components interact among themselves with a given system, as well as interacting with the environment. There is the need for stability and orientation within investment management. This orientation can only be given on a vision level through the statement of the system meaning. As suggested by a cybernetic model of a system, long-term survival of the business depends on having defined sustainable and future-oriented meaning statements. The focus here is on the system usefulness in terms of sustainability involving context of individual, cultural, ethical questions, and expectations within society. Such meaning statements imply that there are values defined for the owner of the investment. In this case, values are one-dimensional for measuring effectiveness. Effectiveness is assessed by quality metric, and efficiency is also measured as a productivity metric. Quality of output, in this perspec-

tive, is related to products or services especially the value assigned by the customers.

Figure 1. Complex cybernetic investment management framework based on OSM.

Beyond the original OSM, two new artifacts are introduced in the system model: Actor and Sensor. The actor has the responsibility to understand certain orders (e.g., management decisions)

considering the economic reality of today [2].

130 Ontology in Information Science

Starting with the abstraction that the probability of each state of a system is 100%, it is possible to model system dynamics and interactions. Any process or change in state of the system is represented as a transformation. We define the function T as one-to-one which means that an initial state S(t + 1) always mapped onto a single state S(t). The function can be used as a dynamical representation to model the interactions between components of a system. The project manager is a system, represented as a Project Manager. He affects the system P or the project itself. We assume that the state of P, at time t + 1, is dependent on the state of the project at time t.

$$T \colon \mathcal{S}\_{\mathsf{PM}} \times \mathcal{S}\_{\mathsf{P}} \to \mathcal{S}\_{\mathsf{P}} \colon (\mathcal{S}\_{\mathsf{PM}}(\mathsf{t}), \mathcal{S}\_{\mathsf{P}}(\mathsf{t})) \to \mathcal{S}\_{\mathsf{P}}(\mathsf{t} + \mathsf{1}) \tag{1}$$

SPM plays the role of the input of the project. In general, the project will not only be affected by an outside system, but also be effected by other systems causing external disturbance (SED).

$$\mathcal{F}' \colon (\mathcal{S}\_{\text{PM}}, \mathcal{S}\_{\text{ED}}) \times \mathcal{S}\_{\text{P}} \to \mathcal{S}\_{\text{P}} \colon \left\{ \mathcal{S}\_{\text{PM}}(t), \mathcal{S}\_{\text{ED}}(t), \mathcal{S}\_{\text{P}}(t) \right\} \to \mathcal{S}\_{\text{P}}(t+1) \tag{2}$$

The project is aimed to produce output which is system SO. This leads to the transition

$$T^\* \colon T^{\prime\prime} \colon (\mathcal{S}\_{\text{PM}}, \mathcal{S}\_{\text{ED}}) \times \mathcal{S}\_{\mathcal{O}} \to \mathcal{S}\_{\mathcal{O}} \colon \{\mathcal{S}\_{\text{PM}}(t), \mathcal{S}\_{\text{ED}}(t), \mathcal{S}\_{\mathcal{O}}(t)\} \tag{3}$$

These decisions must be translated into input parameters. This enables the project SP to change

Ontology in IT Projects Based on OSM http://dx.doi.org/10.5772/intechopen.72729 133

This reflex is also basic for project organization. A team of project experts can be seen as system, SP, while the decision makers as system, SPM. The decision makers can get their guidance through higher level system representations of reason for existing, reason for meaning and basic reason. This system is normally known as steering committee (SSC) (Figure 2). When these systems are logically separate, a hierarchy is evident as shown in a basic organizational model

It is safe to say that each project starts with objectives. These objectives are a combination of different types of statements based on assumptions regarding the environment, E. These are realis moods of domain assumptions describing the environment of the requirements engineering problem as it is known. The second type of statements in objectives is irrealis mood statements describing situation-to-be, how the situation and environment should look like. These irrealis mood statements prescribe what the outcome of the project should be. Figure 3 shows that objectives can be described in functional requirements which are defined as goals (g) and non-functional requirements, which are divided into soft goals (f) and quality constraints, q [5]. With a justified approximation (jappr), it is possible to define softgoal, F, a quality constraint, Q, that can exist for which it is justified to assume that if the quality constraint, Q, is met, then the

into the state t + 1.

3. Ontology of a project system

Figure 2. A basic organizational hierarchical model of a project.

softgoal, F, is also automatically fulfilled as described in

[4].

a project is a process that transforms input into output. If the observer does not know the states of the project, and precise transformations T and T0 , then the project acts as a black box for the observer. By experimenting with the sequence of inputs SPM(t), SPM(t + 1), SPM(t + 2), etc., and observing the corresponding sequence of outputs SO(t + 1), SO (t + 2), SO (t + 3), …, the observer may try to reconstruct the dynamics of the project. In some cases, the observer can determine a state space SP so that both transformations become more deterministic, without being able to directly observe the properties, processes, or components of the project. Internal disturbing make modeling black-box a difficult endeavor.

A project manager is seen as the Observer. This notion is said to hold true for all decisions taken by a project manager in any project management process. Managers see the system as a linear one and try to master the feedback loop. However, since projects are nonlinear in nature, a project manager serving as observer, needs to be more trained to deal with uncertainty of nonlinear systems and focus on positive feedback of the project. This suggests that they might let the system freely float to a certain degree or even intentionally destabilize the system to learn the equilibriums and the resistance to change. Operating at the verge of chaos has been suggested as the most successful strategy to dealing with non-linear systems. Nonlinearity is based on the internal disturbance as well as parallel active processes. This suggests that traditional objectives of a project can have dependencies which are often beyond internal control mechanisms of the system including elements of reason for existing, reason for meaning, and basic reason. In such a case, a non-linear model of OSM is necessary.

At the beginning of the project (t = 0), the project manager provides the necessary input for the project. This input is, in the first place, clearly defined in objectives (i.e., what to deliver). In the second place, project manager provides the scope (i.e., how to deliver), resources (i.e., with what to deliver), and timings (i.e., when to deliver). These inputs are based on the planning of the project. After a control period, the project manager gets information out of the system SP(t) as output SO(t). With this information, the project manager, SPM, can make certain decisions.

Figure 2. A basic organizational hierarchical model of a project.

represented as a transformation. We define the function T as one-to-one which means that an initial state S(t + 1) always mapped onto a single state S(t). The function can be used as a dynamical representation to model the interactions between components of a system. The project manager is a system, represented as a Project Manager. He affects the system P or the project itself. We

SPM plays the role of the input of the project. In general, the project will not only be affected by an outside system, but also be effected by other systems causing external disturbance (SED).

a project is a process that transforms input into output. If the observer does not know the states

observer. By experimenting with the sequence of inputs SPM(t), SPM(t + 1), SPM(t + 2), etc., and observing the corresponding sequence of outputs SO(t + 1), SO (t + 2), SO (t + 3), …, the observer may try to reconstruct the dynamics of the project. In some cases, the observer can determine a state space SP so that both transformations become more deterministic, without being able to directly observe the properties, processes, or components of the project. Internal disturbing

A project manager is seen as the Observer. This notion is said to hold true for all decisions taken by a project manager in any project management process. Managers see the system as a linear one and try to master the feedback loop. However, since projects are nonlinear in nature, a project manager serving as observer, needs to be more trained to deal with uncertainty of nonlinear systems and focus on positive feedback of the project. This suggests that they might let the system freely float to a certain degree or even intentionally destabilize the system to learn the equilibriums and the resistance to change. Operating at the verge of chaos has been suggested as the most successful strategy to dealing with non-linear systems. Nonlinearity is based on the internal disturbance as well as parallel active processes. This suggests that traditional objectives of a project can have dependencies which are often beyond internal control mechanisms of the system including elements of reason for existing, reason for mean-

At the beginning of the project (t = 0), the project manager provides the necessary input for the project. This input is, in the first place, clearly defined in objectives (i.e., what to deliver). In the second place, project manager provides the scope (i.e., how to deliver), resources (i.e., with what to deliver), and timings (i.e., when to deliver). These inputs are based on the planning of the project. After a control period, the project manager gets information out of the system SP(t) as output SO(t). With this information, the project manager, SPM, can make certain decisions.

ing, and basic reason. In such a case, a non-linear model of OSM is necessary.

ð1Þ

ð2Þ

ð3Þ

, then the project acts as a black box for the

assume that the state of P, at time t + 1, is dependent on the state of the project at time t.

The project is aimed to produce output which is system SO. This leads to the transition

of the project, and precise transformations T and T0

132 Ontology in Information Science

make modeling black-box a difficult endeavor.

These decisions must be translated into input parameters. This enables the project SP to change into the state t + 1.

This reflex is also basic for project organization. A team of project experts can be seen as system, SP, while the decision makers as system, SPM. The decision makers can get their guidance through higher level system representations of reason for existing, reason for meaning and basic reason. This system is normally known as steering committee (SSC) (Figure 2). When these systems are logically separate, a hierarchy is evident as shown in a basic organizational model [4].

## 3. Ontology of a project system

It is safe to say that each project starts with objectives. These objectives are a combination of different types of statements based on assumptions regarding the environment, E. These are realis moods of domain assumptions describing the environment of the requirements engineering problem as it is known. The second type of statements in objectives is irrealis mood statements describing situation-to-be, how the situation and environment should look like. These irrealis mood statements prescribe what the outcome of the project should be. Figure 3 shows that objectives can be described in functional requirements which are defined as goals (g) and non-functional requirements, which are divided into soft goals (f) and quality constraints, q [5].

With a justified approximation (jappr), it is possible to define softgoal, F, a quality constraint, Q, that can exist for which it is justified to assume that if the quality constraint, Q, is met, then the softgoal, F, is also automatically fulfilled as described in

This relationship can be described in by . The full set of all deliverables, D, is the result of processes executed by resources with a necessary input. Figure 4 depicts type of expected inputs. This input is either information (both steering information or knowledge

In analogy with the objectives, depicted in the equation above, one can define a justified

We can also define similar requisite relations among budget, manpower, and raw material, as well as some exclude (excl) relation. Additionally, raw material, in each process step after the

With these inputs, output, transformation processes, and steering, one can model the traditional project based working. The transformations follow an environmental adapted approach.

When comparing linear model with the real-life system, one can see that some additional artifacts are necessary to create a more accurate model. As the objectives are defined to contribute to a defined strategy, each transition must bring an added value. As we have defined the transition: with

depends on three processes and internal disturbances as shown below. Internal disturbance is unknown transitions of the systems generating risks. In this case, risk is the possibility of not being able to fulfill the objective as expected and not to deliver the deliverables linked to

This led to an unsolvable equation based on the odd number of functions f and inputs i. This is in fact a characteristic for complex systems. Each of these transitions contributes to the delivery of the expected deliverable. Additionally, the external disturbance can be seen as input iED and depends on unknown environmental changes or changes brought about by stakeholders.

. The transition is not a linear one as assumed above. The transition

This could be traditional waterfall (sequencing) or agile approach (iterative):

ð4Þ

135

Ontology in IT Projects Based on OSM http://dx.doi.org/10.5772/intechopen.72729

ð5Þ

ð6Þ

and expertise) or resources as describes in Figure 4.

the objective:

• Target Core Process (TCP) with

• Internal disturbance (ID) with

• Individual Core Process (ICP) with

• Organizational Core Process (OPC) with

approximation (jappr) between expertise "e," and knowledge, "k".

first process step, is also the deliverable of the first process step.

Figure 3. Types of statements within objectives.

Additionally, one can define a requisite (req) relation as an asymmetric relation defining that in order to make a certain statement true, another statement also needs to be true. This is valid for all statements within requirements (q, g, and s) but not for domain assumptions for requirements. Similar equations are necessary for S (soft goals) and Q for quality constraints. Such requisite relations are important if there are hidden objectives. In this case, there are certain quality constraints, goals, or soft goals identified, which are not directly linked to the objectives, that could be an indicator for an implicit objective based on assumptions. Similar to the requisite relation, an exclude (excl) relation defines a symmetrical relation stating that some statements can exclude each other. These are indicators for conflicting objectives and not valid for quality constraints for requirements. And again, similar equations are necessary for S (soft goals) and Q for quality constraints.

In project work, one can make a separation between domain assumptions, goals, soft goals, and quality constraints as objectives and the realized deliverables as linked output. For all objectives, O, there can be a corresponding objective (rn, dn, sn) at least one deliverable dm within the full set of deliverable, D, to fulfill a given objective. Notice that each deliverable is linked to one or more objectives and is the proven fulfillment of the objective.

Figure 4. Different types of input.

This relationship can be described in by . The full set of all deliverables, D, is the result of processes executed by resources with a necessary input. Figure 4 depicts type of expected inputs. This input is either information (both steering information or knowledge and expertise) or resources as describes in Figure 4.

In analogy with the objectives, depicted in the equation above, one can define a justified approximation (jappr) between expertise "e," and knowledge, "k".

$$(apppr(o,k)\Rightarrow(o\Rightarrow k)for(all\ e\ in E,k\in\mathcal{K}))\tag{4}$$

We can also define similar requisite relations among budget, manpower, and raw material, as well as some exclude (excl) relation. Additionally, raw material, in each process step after the first process step, is also the deliverable of the first process step.

$$\forall i \in I \left\{ n \ge 1, t\_{n+1} = (t\_{n\text{tot}\_n} + t\_{n\text{tot}\_n} + t\_{n\text{tot}\_n} + t\_{k\_n} + t\_{\sigma\_n} + t\_{\sigma\_n} \right\} \tag{5}$$

$$\forall i \in I, t\_{n+1} = (t\_{n\text{tot}\_{n+1}} + t\_n) + t\_{n\text{tot}\_{n+1}} + t\_{n\text{tot}\_{n+1}} + t\_{k\_{n+1}} + t\_{\sigma\_{n+1}} \tag{6}$$

With these inputs, output, transformation processes, and steering, one can model the traditional project based working. The transformations follow an environmental adapted approach. This could be traditional waterfall (sequencing) or agile approach (iterative):

$$\forall \; d \in \mathcal{D} \left\{ \begin{array}{c} n = \mathbf{1} \; d\_n = f(\; t\_n, o\_n) \; \text{with } t \in T \\ n > \mathbf{1} \; t\_{n+1} = \; d\_n = f(\; t\_n, o\_n, d\_{n-1}) \; \text{with } t \in T \end{array} \right. \tag{6}$$

When comparing linear model with the real-life system, one can see that some additional artifacts are necessary to create a more accurate model. As the objectives are defined to contribute to a defined strategy, each transition must bring an added value. As we have defined the transition: with . The transition is not a linear one as assumed above. The transition depends on three processes and internal disturbances as shown below. Internal disturbance is unknown transitions of the systems generating risks. In this case, risk is the possibility of not being able to fulfill the objective as expected and not to deliver the deliverables linked to the objective:

• Target Core Process (TCP) with

Additionally, one can define a requisite (req) relation as an asymmetric relation defining that in order to make a certain statement true, another statement also needs to be true. This is valid for all statements within requirements (q, g, and s) but not for domain assumptions

(soft goals) and Q for quality constraints. Such requisite relations are important if there are hidden objectives. In this case, there are certain quality constraints, goals, or soft goals identified, which are not directly linked to the objectives, that could be an indicator for an implicit objective based on assumptions. Similar to the requisite relation, an exclude (excl) relation defines a symmetrical relation stating that some statements can exclude each other. These are indicators for conflicting objectives and not valid for quality constraints

In project work, one can make a separation between domain assumptions, goals, soft goals, and quality constraints as objectives and the realized deliverables as linked output. For all objectives, O, there can be a corresponding objective (rn, dn, sn) at least one deliverable dm within the full set of deliverable, D, to fulfill a given objective. Notice that each deliverable is

linked to one or more objectives and is the proven fulfillment of the objective.

are necessary for S (soft goals) and Q for quality constraints.

Figure 3. Types of statements within objectives.

134 Ontology in Information Science

Figure 4. Different types of input.

for requirements. Similar equations are necessary for S

for requirements. And again, similar equations

This led to an unsolvable equation based on the odd number of functions f and inputs i. This is in fact a characteristic for complex systems. Each of these transitions contributes to the delivery of the expected deliverable. Additionally, the external disturbance can be seen as input iED and depends on unknown environmental changes or changes brought about by stakeholders.

$$H\_{\rm ED} \rightarrow \ \Delta Envtronnment \ or \ \Delta Stakeholders \tag{7}$$

This input can also be seen as a risk, since it is unknown to the system and similar modeled as other inputs.

Objectives are derived from strategic drivers. These strategic drivers, sd, are the concretization (detailed description) of the strategy, S. Each strategy has at least one strategic driver and at least one objective. The contribution of the objective to the strategic driver is an estimation, since the use of the deliverables linked to the objective is in the future. This estimation is mostly done in a matrix with value areas so that the contribution can be estimated [6].

The result of the estimation is mainly in odd categories (e.g., low, medium, and high) added by "non." For each estimated contribution, a financial analysis of efficiency (e.g., Net Present Value and Return On Investment) is done to estimate the revenue. Within this calculation, a link is made among the necessary input of the project, the expected delivery, and the estimated revenue. This is done in the Business Case. This Business Case is the economical reason for existing (i.e., RFE) of the project. The calculation is based on the link between all elements of the input, I, necessary for all deliverables, d, linked to objective, o, in relation to the estimated contribution [7].

$$RFE \hookrightarrow \bigcup f\left(\mathfrak{p}\_{(o\_n, sd\_m)}, I\_n, d\_n, o\_n, t\right) \quad \forall d \in \mathcal{D}, \forall o \in \mathcal{O} \text{ and } I \to \mathcal{D} \tag{8}$$

As scope, timing, and resources (input) are related to each other by the "Devil's triangle," we can calculate each point as a variable of the two other points leading to the function to calculate the timings of the project:

$$\mathbf{t} \models f\left(\mathbf{\hat{p}}\_{(o\_n, sd\_m)}, I\_t, d\_n, \mathbf{o}\_n, \mathbf{R}FE\right) \tag{9}$$

possibility for quantifiable, measurable, and observable parameters. Additionally, all needed measurements need to be covered by having parameters concerning output, people, and

Vision can be described as an image or description of the customers' system after the implementation of the deliverables of the project. In this vision, a strategy is essential and takes different forms. It requires the use of strategic drivers to translate the strategy into the project objectives. Traditional approaches, as described above, or the use of moving averages can be

The Sensor must be able to measure all outputs of the system. In this case, a Sensor, s, of the Target Core Process (Sensor TCP) extracts all the necessary information from the system and prepares this information for management decision of the project manager who deals with system feedback: , with

The Sensor, s, for the Individual Core Process (SensorICP) and for the Organizational Core Process (SensorOCP) identifies, similar to the Sensor TCP, data according to parameters defined.

, and .

Ontology in IT Projects Based on OSM http://dx.doi.org/10.5772/intechopen.72729 137

used to define the strategic drivers, which are shown in Figure 6.

organization [8].

Figure 6. Strategic drivers.

Figure 5. Statements in a Business Case.

This unsolvable loop of equations can be approximated by different iterations and adapted estimations during the project definition. In case a level of uncertainty (at the stakeholders) is reached, the iterations will stop and the project is defined with all necessary information to execute the transition, T (process all input I to the desired deliverables D). The system is ready defined by Objectives , expected deliverables , Scope (transitions), , Resources and input , Risks (external and internal disturbance) TID and iED as well as Timings . Similar to the definition of the link between objective and deliverable as .

The link between objective and strategic driver/strategy, objective and Business Case, strategic driver/strategy and Mission, and Mission and Vision can be described similar. Mission is a foundational statement that describes the purpose of projects existence. It answers questions such as "why do we do what we do" and "who do we serve." For each project, this is the Business Case that should distinguish one project from another. Within a Business Case, different statements are made, as seen in Figure 5. Beside measurable economical facts, there is a

Figure 5. Statements in a Business Case.

ð7Þ

ð8Þ

ð9Þ

This input can also be seen as a risk, since it is unknown to the system and similar modeled as

Objectives are derived from strategic drivers. These strategic drivers, sd, are the concretization (detailed description) of the strategy, S. Each strategy has at least one strategic driver and at least one objective. The contribution of the objective to the strategic driver is an estimation, since the use of the deliverables linked to the objective is in the future. This estimation is mostly

The result of the estimation is mainly in odd categories (e.g., low, medium, and high) added by "non." For each estimated contribution, a financial analysis of efficiency (e.g., Net Present Value and Return On Investment) is done to estimate the revenue. Within this calculation, a link is made among the necessary input of the project, the expected delivery, and the estimated revenue. This is done in the Business Case. This Business Case is the economical reason for existing (i.e., RFE) of the project. The calculation is based on the link between all elements of the input, I, necessary for all deliverables, d, linked to objective, o, in relation to the estimated

As scope, timing, and resources (input) are related to each other by the "Devil's triangle," we can calculate each point as a variable of the two other points leading to the function to calculate

This unsolvable loop of equations can be approximated by different iterations and adapted estimations during the project definition. In case a level of uncertainty (at the stakeholders) is reached, the iterations will stop and the project is defined with all necessary information to execute the transition, T (process all input I to the desired deliverables D). The system is ready defined by Objectives , expected deliverables , Scope (transitions),

TID and iED as well as Timings . Similar to the definition of the link

The link between objective and strategic driver/strategy, objective and Business Case, strategic driver/strategy and Mission, and Mission and Vision can be described similar. Mission is a foundational statement that describes the purpose of projects existence. It answers questions such as "why do we do what we do" and "who do we serve." For each project, this is the Business Case that should distinguish one project from another. Within a Business Case, different statements are made, as seen in Figure 5. Beside measurable economical facts, there is a

between objective and deliverable as .

, Resources and input , Risks (external and internal disturbance)

done in a matrix with value areas so that the contribution can be estimated [6].

other inputs.

136 Ontology in Information Science

contribution [7].

the timings of the project:

possibility for quantifiable, measurable, and observable parameters. Additionally, all needed measurements need to be covered by having parameters concerning output, people, and organization [8].

Vision can be described as an image or description of the customers' system after the implementation of the deliverables of the project. In this vision, a strategy is essential and takes different forms. It requires the use of strategic drivers to translate the strategy into the project objectives. Traditional approaches, as described above, or the use of moving averages can be used to define the strategic drivers, which are shown in Figure 6.

The Sensor must be able to measure all outputs of the system. In this case, a Sensor, s, of the Target Core Process (Sensor TCP) extracts all the necessary information from the system and prepares this information for management decision of the project manager who deals with system feedback: , with , and .

The Sensor, s, for the Individual Core Process (SensorICP) and for the Organizational Core Process (SensorOCP) identifies, similar to the Sensor TCP, data according to parameters defined.

Figure 6. Strategic drivers.

As these are not always objective, other measurements are necessary. For the SensorICP possible examples are, among others, Employee Satisfaction Surveys, Bilateral meetings, Coaching moments, Evaluations, and follow-up discussions.

For the SensorOCP possible examples are, among others, Outstanding reorganizations, "Over the fence" management of departments, and Leadership Decision Management Systems.

In reality, most of the sensors are focused on TCP second on OCP, and there is a minor group measuring the ICP. In opposite to that, studies show that the organizational aspects as well as the individual integration in the team are significantly impacting the result of the project. This is even more visible when looking the output in general. The output of the system is not only a set of deliverables but also changes in knowledge, experiences, and other insights [9].

As the information of the Sensor is multidimensional, it can be seen as a vector of information. The project manager executes a transformation of the information to create an input to the system:

Figure 8. Class model for deliverables, objectives, reason for existing, and vision.

In this transition, TPM, project manager, SPM, converts the information of the Sensor into input of the system. All necessary processes can be found in process-based method for effective project management. The output of this processes will be injected back into the system project SP by a translation f(IP). This translation helps in ensuring that missing resources are injected

In this section, we provide an application of the developed model. A non-disclosure agreement prevents us from disclosing certain detailed information regarding the company (entity) of analysis. Therefore, some information is masked. However, we provide sufficient information to demonstrate the usability of the model. Within this company, different and sometime conflicting core activities are present. The global mission statement is translated into global strategic statements within each business unit and has a defined strategy for fulfilling the requested group strategy. Measurable strategic drivers are given and defined as Performance (mainly driven by Cost/income ration), Empowerment (mainly driven by maturity levels), Accountability (mainly cultural driven), Responsiveness (time2market), and local embeddedness (local compliance).

Additionally, the company wants to "aim to be the first company TOMORROW to be the reference … TODAY." The given drivers on group level are defined on coarse grain level. Each Unit determined their own strategy to fulfill the expected groups' strategy. Therefore, they defined meso-grain drivers to evaluate their own operations and investments, the so called "Rose." This level is the strategy of the Business Unit and represents the next detailed level.

At this level, the link to the Business Case is already implemented as drivers for the investment analysis as analysis of efficiency or Business Case. The other drivers are defined for the purposes of ranking initiatives according to an added value without executing a detailed Business Case called back-log. These drivers are separated into two groups (compare Figure 9). The first group is the "Externally drivers" and provides KPIs related to the company outside world, while the

others took on "Internal aspects" of the organization.

or that more material is provided.

4. Briefly use case

with .

Ontology in IT Projects Based on OSM http://dx.doi.org/10.5772/intechopen.72729 139

However, different measurements can be combined and considered in a multi-dimensional matrix. The change periods, CP, is related with each other. An exact relation depends on the environment. Obvious in fast changes environments (e.g., mobile application development) the period is much shorter than in slow changing environments (e.g., industrial plant investments). This discussion provides a basis for linking different elements including sensors and system vision. A systematic overview of possible sensors is given in Figure 7.

Up to this time, time aspect of dependency has been overlooked. However, this aspect is introduced by the Business Case which accounts for maximum in project duration and the return period. For the project, Business Case is translated into a "plan in time." The plan is part of the steering and is indicated by the dotted line (box). Taking the picture Figure 7, one can reduce the information into a class model Figure 8 to be able to design the system.

Figure 7. Relating sensor and deliverables, objectives, reason for existing, mission as well as vision.

Figure 8. Class model for deliverables, objectives, reason for existing, and vision.

As the information of the Sensor is multidimensional, it can be seen as a vector of information. The project manager executes a transformation of the information to create an input to the system:

$$\overrightarrow{INF\_{act,t}} = \begin{pmatrix} \textit{int}\_{(d),t} \\ \textit{int}\_{(a),t} \\ \vdots \end{pmatrix} \text{ and } \overrightarrow{INF\_{ray,t}} = \begin{pmatrix} t\_x \\ \vdots \\ t\_y \end{pmatrix} \text{ with } \overrightarrow{INF\_{ray,t}} = \mathcal{S}\_{PM} \times \overrightarrow{INF\_{act,t}} \text{ with } \mathcal{S}\_{PM} = \begin{pmatrix} t\_x \\ t\_y \\ \vdots \\ t\_n \end{pmatrix} \text{ with } \overrightarrow{PN\_{PW}} = \begin{pmatrix} \textit{int}\_{(a),t} \\ \vdots \\ \textit{int}\_{(b)} \end{pmatrix} \text{ with } \textit{return } \overrightarrow{p\_{SW}} = \begin{pmatrix} \textit{int}\_{(a),t} \\ \textit{int}\_{(b),t} \\ \vdots \end{pmatrix} \text{ with } \textit{return } \overrightarrow{p\_{SW}} = \begin{pmatrix} \textit{int}\_{(a),t} \\ \textit{int}\_{(b),t} \\ \textbf{int}\_{(c),t} \end{pmatrix}$$

In this transition, TPM, project manager, SPM, converts the information of the Sensor into input of the system. All necessary processes can be found in process-based method for effective project management. The output of this processes will be injected back into the system project SP by a translation f(IP). This translation helps in ensuring that missing resources are injected or that more material is provided.

#### 4. Briefly use case

As these are not always objective, other measurements are necessary. For the SensorICP possible examples are, among others, Employee Satisfaction Surveys, Bilateral meetings, Coaching

For the SensorOCP possible examples are, among others, Outstanding reorganizations, "Over the fence" management of departments, and Leadership Decision Management Systems.

In reality, most of the sensors are focused on TCP second on OCP, and there is a minor group measuring the ICP. In opposite to that, studies show that the organizational aspects as well as the individual integration in the team are significantly impacting the result of the project. This is even more visible when looking the output in general. The output of the system is not only a

However, different measurements can be combined and considered in a multi-dimensional matrix. The change periods, CP, is related with each other. An exact relation depends on the environment. Obvious in fast changes environments (e.g., mobile application development) the period is much shorter than in slow changing environments (e.g., industrial plant investments). This discussion provides a basis for linking different elements including sensors and

Up to this time, time aspect of dependency has been overlooked. However, this aspect is introduced by the Business Case which accounts for maximum in project duration and the return period. For the project, Business Case is translated into a "plan in time." The plan is part of the steering and is indicated by the dotted line (box). Taking the picture Figure 7, one can

set of deliverables but also changes in knowledge, experiences, and other insights [9].

system vision. A systematic overview of possible sensors is given in Figure 7.

reduce the information into a class model Figure 8 to be able to design the system.

Figure 7. Relating sensor and deliverables, objectives, reason for existing, mission as well as vision.

moments, Evaluations, and follow-up discussions.

138 Ontology in Information Science

In this section, we provide an application of the developed model. A non-disclosure agreement prevents us from disclosing certain detailed information regarding the company (entity) of analysis. Therefore, some information is masked. However, we provide sufficient information to demonstrate the usability of the model. Within this company, different and sometime conflicting core activities are present. The global mission statement is translated into global strategic statements within each business unit and has a defined strategy for fulfilling the requested group strategy. Measurable strategic drivers are given and defined as Performance (mainly driven by Cost/income ration), Empowerment (mainly driven by maturity levels), Accountability (mainly cultural driven), Responsiveness (time2market), and local embeddedness (local compliance).

Additionally, the company wants to "aim to be the first company TOMORROW to be the reference … TODAY." The given drivers on group level are defined on coarse grain level. Each Unit determined their own strategy to fulfill the expected groups' strategy. Therefore, they defined meso-grain drivers to evaluate their own operations and investments, the so called "Rose." This level is the strategy of the Business Unit and represents the next detailed level.

At this level, the link to the Business Case is already implemented as drivers for the investment analysis as analysis of efficiency or Business Case. The other drivers are defined for the purposes of ranking initiatives according to an added value without executing a detailed Business Case called back-log. These drivers are separated into two groups (compare Figure 9). The first group is the "Externally drivers" and provides KPIs related to the company outside world, while the others took on "Internal aspects" of the organization.

The methodology can be seen in Figure 11, but on confidentiality reasons the detail-content is

Ontology in IT Projects Based on OSM http://dx.doi.org/10.5772/intechopen.72729 141

Figure 11. Project links among sensor and deliverables, objectives, reason for existing, mission and vision.

Figure 12. An example of statements in the case of starting from mission statement.

Figure 13. A second example of statements in a case starting from mission statements.

At this level, we have identified several projects scope items with the exclusion of approximate relations between drivers. Interestingly, as some IT-related drivers are fulfilled, Business Unit drivers are also fulfilled. However, there can be contradictions among drivers. This is the case when, in one project, the meso-grain drivers are detailed into fine-grain drivers within the Business-Case. Figure 12 shows an example for strategic statements. The first statement was "to become the first with easy access techniques for the customers." This follows the driver "to be the reference and the first of this easy-access today." On the next level, the management decided that "using other identification and authentication technique" allows to assume (justified approximation)

blanked.

Figure 9. An example of statements, the case emerging from mission statement.

Figure 10. A list of strategic drivers used in a business unit and IT-related drivers.

Besides business (BUS)-related drivers, the company also has information and technology (IT) related drivers. These drivers are also defined on strategic level. Figure 10 shows a mapping indicating where drivers are complementary and where the drivers are supplementary.

As previously mentioned, all cases are listed in a "back-log." In this back-log, all scope-items are listed and are prioritized according the drivers of the Business Unit as indicated above. While some of these items can be executed separately, others require a "project" approach. To define the priority, the value of each driver is defined and described along with the degree of performance.

The use of this methodology provides insights into the expected deliverables as well as the objectives. The deliverables are mentioned as a single line and describe the expected outcome of the task as clusters defining objectives. The objectives are mentioned as numbers in the list.

Figure 11. Project links among sensor and deliverables, objectives, reason for existing, mission and vision.

The methodology can be seen in Figure 11, but on confidentiality reasons the detail-content is blanked.

At this level, we have identified several projects scope items with the exclusion of approximate relations between drivers. Interestingly, as some IT-related drivers are fulfilled, Business Unit drivers are also fulfilled. However, there can be contradictions among drivers. This is the case when, in one project, the meso-grain drivers are detailed into fine-grain drivers within the Business-Case. Figure 12 shows an example for strategic statements. The first statement was "to become the first with easy access techniques for the customers." This follows the driver "to be the reference and the first of this easy-access today." On the next level, the management decided that "using other identification and authentication technique" allows to assume (justified approximation)

Figure 12. An example of statements in the case of starting from mission statement.

Besides business (BUS)-related drivers, the company also has information and technology (IT) related drivers. These drivers are also defined on strategic level. Figure 10 shows a mapping indicating where drivers are complementary and where the drivers are supplementary.

Figure 9. An example of statements, the case emerging from mission statement.

Figure 10. A list of strategic drivers used in a business unit and IT-related drivers.

As previously mentioned, all cases are listed in a "back-log." In this back-log, all scope-items are listed and are prioritized according the drivers of the Business Unit as indicated above. While some of these items can be executed separately, others require a "project" approach. To define the priority, the value of each driver is defined and described along with the degree of

The use of this methodology provides insights into the expected deliverables as well as the objectives. The deliverables are mentioned as a single line and describe the expected outcome of the task as clusters defining objectives. The objectives are mentioned as numbers in the list.

performance.

140 Ontology in Information Science

Figure 13. A second example of statements in a case starting from mission statements.

that these techniques will increase the easy access. Finally, a similar assumption is made to "use voice and face recognitions for identification."

example. In the case study, two different projects were defined from the "back-log" and the dilemma, as shown in Figure 14. One project is a local implementation on the same SharePoint tenant as an international implementation. However, both projects are defining their case

Ontology in IT Projects Based on OSM http://dx.doi.org/10.5772/intechopen.72729 143

However, during the local translations and the concrete implementation in the BUS-Case, both the projects used different statements, d2 and g2. Obviously, both PM's have different ways to deal with the issue of the excluding statements d2 (passing translations and Multilanguage) and g2 (maximizing reusability) to reach the statement d1 (decreasing costs). Each might view the other statement "d2" or reverse "g2" (the decision) as a disturbance, and therefore try to mitigate its impact. On the other hand, both PM's could elevate the issue to a higher level by using appropriate feedback loops. In the case study, both PM's decided to escalate issue via their steering committee. These committees decided to escalate on an international level, where problems could be solved. This approach was taken since projects where able to show logical link of their own drivers to the drivers of the Business Unit and drivers on group level. The group steering committee decided that reusability should be implemented. This enabled savings of the second project to be used as subsidies to the first project. As expected, both projects received changes to the inputs including change in scope, change in budget, and

The discussed model provides a clear overview about influencing parameters, disturbances, and processes executed during a project. The offered more robust model for project management in complex situations allows the project manager to understand more the role of project management and provides possible steering once the system is implemented in real projects.

The model not only proved the usability but also shows the effort necessary to be implemented. As the elements of the system are not clear enough in the beginning, the implementation takes some experience and time to be valid enough for use. Within the presented case, the usability of the model was reached after 2 years and proves the value in prediction of the impact of steering. This means a more practical implementation guide as well as supporting documentation is necessary to use this model more in practice. Here, additional work is

Within a brief use-case, the focus on internal organizational complexity is shown.

separately as described.

change in resources.

5. Conclusion

necessary.

Author details

Address all correspondence to<EMAIL_ADDRESS>
KU Leuven, KBC Group NV, Mechelen, Belgium

Frank Stumpe

Similar to this depicts another strategic statement (Figure 13). Here, the company is obligated to fulfill the regulators request to be able provide legal provable identification and authentication. This follows the local embeddedness and compliance with the local regulations. The next management level assumed that only using legal accepted techniques can give evidence that the mission statement is fulfilled. This statement leads to the conclusion that old existing technique is actually the only technique for fulfilling the statement.

Taking e1 and f2 as independent from each other in one system, the mission statement is without exclusions. On the next level, one sees that the statements are excluding each other excl (f2, f2) and excl (f3, e3)]. If a project, in this case, starts to increase easy access to the system, then the same project will be confronted with statements which will not allow the project to deliver successfully. The project manager needs to detect this exclusion and create another input into the system. This input is considered new information coming externally as information from another system, the company SED. The project itself faces the problem (SP) and the Project Manager transforms this into the scope change (SO). We can conclude that the project manager transforms information received, the exclusions, to allow for a change in scope, SO, which results into a new scope, SO(t + 1), as described. The project will deal with the highest exclusion. This means dealing with clarification of any legal provisions and authentication techniques. Once the exclusion of e2 and f2 excl (f2, e2)] is met, the project will deliver a solution which fits more to the mission of the company.

Another example of a project is a company deciding to respond to local markets with local front-ends in local language. This should increase the local embeddedness and time2market as the local developers do not need to translate. Subsequently, such a project reduces costs for the company. This type of a project also presents a unique opportunity in terms of using the same solution in other markets (i.e., regions and countries) that uses a different language, for

Figure 14. Two project sequences in different entities using the same mission statement.

example. In the case study, two different projects were defined from the "back-log" and the dilemma, as shown in Figure 14. One project is a local implementation on the same SharePoint tenant as an international implementation. However, both projects are defining their case separately as described.

However, during the local translations and the concrete implementation in the BUS-Case, both the projects used different statements, d2 and g2. Obviously, both PM's have different ways to deal with the issue of the excluding statements d2 (passing translations and Multilanguage) and g2 (maximizing reusability) to reach the statement d1 (decreasing costs). Each might view the other statement "d2" or reverse "g2" (the decision) as a disturbance, and therefore try to mitigate its impact. On the other hand, both PM's could elevate the issue to a higher level by using appropriate feedback loops. In the case study, both PM's decided to escalate issue via their steering committee. These committees decided to escalate on an international level, where problems could be solved. This approach was taken since projects where able to show logical link of their own drivers to the drivers of the Business Unit and drivers on group level. The group steering committee decided that reusability should be implemented. This enabled savings of the second project to be used as subsidies to the first project. As expected, both projects received changes to the inputs including change in scope, change in budget, and change in resources.

## 5. Conclusion

that these techniques will increase the easy access. Finally, a similar assumption is made to "use

Similar to this depicts another strategic statement (Figure 13). Here, the company is obligated to fulfill the regulators request to be able provide legal provable identification and authentication. This follows the local embeddedness and compliance with the local regulations. The next management level assumed that only using legal accepted techniques can give evidence that the mission statement is fulfilled. This statement leads to the conclusion that old existing

Taking e1 and f2 as independent from each other in one system, the mission statement is without exclusions. On the next level, one sees that the statements are excluding each other excl (f2, f2) and excl (f3, e3)]. If a project, in this case, starts to increase easy access to the system, then the same project will be confronted with statements which will not allow the project to deliver successfully. The project manager needs to detect this exclusion and create another input into the system. This input is considered new information coming externally as information from another system, the company SED. The project itself faces the problem (SP) and the Project Manager transforms this into the scope change (SO). We can conclude that the project manager transforms information received, the exclusions, to allow for a change in scope, SO, which results into a new scope, SO(t + 1), as described. The project will deal with the highest exclusion. This means dealing with clarification of any legal provisions and authentication techniques. Once the exclusion of e2 and f2 excl (f2, e2)] is met, the project will deliver a solution

Another example of a project is a company deciding to respond to local markets with local front-ends in local language. This should increase the local embeddedness and time2market as the local developers do not need to translate. Subsequently, such a project reduces costs for the company. This type of a project also presents a unique opportunity in terms of using the same solution in other markets (i.e., regions and countries) that uses a different language, for

voice and face recognitions for identification."

142 Ontology in Information Science

which fits more to the mission of the company.

technique is actually the only technique for fulfilling the statement.

Figure 14. Two project sequences in different entities using the same mission statement.

The discussed model provides a clear overview about influencing parameters, disturbances, and processes executed during a project. The offered more robust model for project management in complex situations allows the project manager to understand more the role of project management and provides possible steering once the system is implemented in real projects. Within a brief use-case, the focus on internal organizational complexity is shown.

The model not only proved the usability but also shows the effort necessary to be implemented. As the elements of the system are not clear enough in the beginning, the implementation takes some experience and time to be valid enough for use. Within the presented case, the usability of the model was reached after 2 years and proves the value in prediction of the impact of steering. This means a more practical implementation guide as well as supporting documentation is necessary to use this model more in practice. Here, additional work is necessary.

## Author details

Frank Stumpe

Address all correspondence to<EMAIL_ADDRESS>
KU Leuven, KBC Group NV, Mechelen, Belgium

## References

[1] Schwaninger M. System theory and cybernetics: A solid basis for transdisciplinarity in management education and research. Kybernetes. 2001;30(9/10):1209-1222

**Chapter 7**

**Provisional chapter**

**Ontology: Core Process Mining and Querying Enabling**

Ontology permits the addition of semantics to process models derived from mining the various data stored in many information systems. The ontological schema enables for automated querying and inference of useful knowledge from the different domain processes. Indeed, such conceptualization methods particularly *ontologies for process management* which is currently allied to *semantic process mining* trails to combine process models with ontologies, and are increasingly gaining attention in recent years. In view of that, this chapter introduces an ontology-based mining approach that makes use of concepts within the extracted event logs about domain processes to propose a method which allows for effective querying and improved analysis of the resulting models through semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner). The proposed method is a semantic-based process mining approach that is able to induce new knowledge based on previously unobserved behaviours, and a more intuitive and easy way to represent and query the datasets and the discovered models compared to other standard logical procedures. To this end, the study claims that it is possible to apply effective reasoning methods to make inferences over a process knowledge-base (e.g. the learning process) that leads to automated discovery of learning

**Ontology: Core Process Mining and Querying**

DOI: 10.5772/intechopen.71981

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

© 2018 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

Ontologies has been proven to be one of the essential tools used for semantic-based process mining. The schema is a useful technique towards improving information values of process

**Keywords:** ontologies, semantic annotation, semantic reasoning, process querying,

Kingsley Okoye, Syed Islam and Usman Naeem

Kingsley Okoye, Syed Islam and Usman Naeem

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71981

patterns and/or behaviour.

**1. Introduction**

process mining, event logs, process models

**Tool**

**Enabling Tool**

**Abstract**

#### **Ontology: Core Process Mining and Querying Enabling Tool Ontology: Core Process Mining and Querying Enabling Tool**

DOI: 10.5772/intechopen.71981

References

144 Ontology in Information Science

[1] Schwaninger M. System theory and cybernetics: A solid basis for transdisciplinarity in

[2] Crownover MWB. Complex system contextual framework (CSCF): A grounded-theory construction for the articulation of system context in addressing complex systems prob-

[3] Stumpe F, Polinpapilinho FK. A cybernetic ontology for project management. International Journal of System of Systems Engineering. 2017;8(1):42-73. DOI: 10.1504/IJSSE.2017.083937

[4] Hartman F, Ashrafi RA. Project management in the information systems and information

[5] Huysegoms T, Snoeck M, Dedene G, Goderis A, Stumpe F. Visualizing variability management in requirements engineering through formal concept analysis. Procedia Technol-

[6] Keating CB, Katina PF, Bradley JM. Complex system governance: Concept, challenges, and emerging research. International Journal of System of Systems Engineering. 2014;5(3):263-288

[7] Stumpe F. Entwicklung und betrieb von neuen sattelanhänger-waggon kombinationen für zukunftsfähige intermodale transportketten in Europa [Dissertation]. Aachen: RWTH Aachen University; 2003. Retrieved from: http://publications.rwth-aachen.de/record/62709

[8] Stumpe F. Uniformity in project-based working. The Journal of Modern Project Manage-

[9] Katina PF, Keating CB, Jaradat RM. System requirements engineering in complex situations. Requirements Engineering. 2014;19(1):45-62. DOI: 10.1007/s00766-012-0157-0

lems [Dissertation]. Norfolk, VA, United States: Old Dominion University; 2005

management education and research. Kybernetes. 2001;30(9/10):1209-1222

technologies industries. Project Management Journal. 2002;33(3):5-15

ogy. 2013;9:189-199. DOI: 10.1016/j.protcy.2013.12.021

ment JMPM - Number 5 - September/December. 2014;2(2)

Kingsley Okoye, Syed Islam and Usman Naeem Kingsley Okoye, Syed Islam and Usman Naeem

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71981

#### **Abstract**

Ontology permits the addition of semantics to process models derived from mining the various data stored in many information systems. The ontological schema enables for automated querying and inference of useful knowledge from the different domain processes. Indeed, such conceptualization methods particularly *ontologies for process management* which is currently allied to *semantic process mining* trails to combine process models with ontologies, and are increasingly gaining attention in recent years. In view of that, this chapter introduces an ontology-based mining approach that makes use of concepts within the extracted event logs about domain processes to propose a method which allows for effective querying and improved analysis of the resulting models through semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner). The proposed method is a semantic-based process mining approach that is able to induce new knowledge based on previously unobserved behaviours, and a more intuitive and easy way to represent and query the datasets and the discovered models compared to other standard logical procedures. To this end, the study claims that it is possible to apply effective reasoning methods to make inferences over a process knowledge-base (e.g. the learning process) that leads to automated discovery of learning patterns and/or behaviour.

**Keywords:** ontologies, semantic annotation, semantic reasoning, process querying, process mining, event logs, process models

## **1. Introduction**

Ontologies has been proven to be one of the essential tools used for semantic-based process mining. The schema is a useful technique towards improving information values of process

and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, © 2018 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

models and their analysis by means of conceptualization. The conceptual system of analysis allows the meaning of process elements to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge that could be used to determine useful patterns as well as predict future outcomes.

descriptions and/or reformulation of the meanings of the labels within the event logs and process models, as well as their comparisons for the purpose of improving the usefulness and performance of the entire domain processes in question particularly during the information retrieval, processing, and extraction process. In short, the propose approach in this chapter supports the augmentation of the informative values of the resulting models by semantically annotating the process elements with concepts they represent in real time, and linking them to an ontology in order to allow for analysis of the extracted data logs and models at a much

Ontology: Core Process Mining and Querying Enabling Tool

http://dx.doi.org/10.5772/intechopen.71981

147

In turn, the conceptual method of analysis provides an easy way to analyse the datasets (i.e. the event logs and models), and even more allows the meaning of the process elements to be enhanced through the use of property descriptions languages or syntax—such as the Ontology Web-Rule Language (OWL) [7] Semantic Web Rule Language (SWRL) [8], Description Logic (DL) queries [9], and classification of discoverable entities or taxonomy [4] in order to make available inference knowledge that could be utilized to determine useful patterns by means of the semantic reasoning aptitudes. On the other hand, the semantic modelling (*ontological representations*) and analysis techniques provide us with the opportunity to develop intelligent algorithms and tools which are capable of enhancing the resulting process models through explicit specification of the concepts (often referred to as *conceptualisation*) [5, 10, 11] in order to identify appropriate domain semantics and relationships amongst the process elements.

Finally, the work applies the proposed method on the case study of learning process domain to demonstrate the usefulness of the semantic-based approach. The study takes into consideration the different stages of process mining and its application—from the initial phase of collecting and transformation of the readily available event data to discovered process models, and then to semantically preparing the extracted models for further analysis and process querying at a more abstraction level. In essence, the chapter shows by using the case study of *Learning Process*—how the data from the various process domains can be extracted, semantically prepared, and transformed into mining executable formats to support the discovery, monitoring and enhancement of real-time processes through further semantic analysis of the discovered models. Indeed, the proposals and outcomes of the study shows that a system which is formally encoded with semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner) has the capability to enhance process mining

Over the following section, the study looks at the ontological concepts and its main functions, and the describe how the work has utilised the schema to develop the proposed semantic-

As a collection of *concepts* and *predicates*, ontology has the ability to perform logic reasoning and bridge the underlying challenges (semantic gaps) beneath event logs and models discovered especially through conventional process mining techniques with rich semantics.

analysis and results from the syntactic level to a much more conceptual level.

more conceptual level.

based process mining approach.

**2. Ontologies**

Indeed, the ability to mine useful or worthwhile knowledge from readily extracted data in current information systems is a challenge, due to the exponential increase in volume of data that is continuously generated. Moreover, many of such organizations data collection systems and procedures for the process analysis is proving to be more and more complex. In consequence, this has spanned the need for a richer or advanced description of real-time processes that allows for flexible exploration of the large volumes of data targeted at improving the systems performance and of course the main business operations. Such process-related analysis means there is also need for techniques that are capable of extracting valuable information from the event logs and the resulting models about the real time processes in view.

More or less, most organization have invested in projects to model their various operational process. However, most of the derived process models are often unfitting, non-operational, or represents a form of reality that are pointed towards comprehensibility rather than covering the entire actual business process complexities. Perhaps, according to the works in Refs. [1–4] an accurate exploration or analysis of the extracted events log is capable of providing vital and valuable information with regards to the quality of support being offered for the so-called organizations and their information knowledge-base or system at large. For example, revealing the underlying relationships the process elements or resources share amongst themselves within the information knowledge-base.

Recently, the Process Mining [3] or yet still Process Querying [5] notion has become a valuable technique used to discover such kind of meaningful information from the event data logs and the derived process models. However, the study carried out in [6] observes that a shared challenge with most of the existing process mining techniques is that they depend on tags/labels in event logs information about the processes they represent, and therefore, to a certain extent are limited because they lack the abstraction level required from real world perspectives. This means that the techniques do not technically gain from the real knowledge (semantics) that describe the tags or labels in events log of the domain processes [6]. Practically, majority of the process mining techniques in literature are purely syntactic in nature, and to this effect are somewhat vague when confronted with unstructured data.

For that reason, this work explores the technological potentials and prospects in using ontology as a core process mining and querying enabling tool by pursuing to address such challenges posed by the lack of semantic information through provision of a method for formal structuring of the readily available datasets. In other words, the work in this chapter addresses the above challenges i.e. (i) lack of process mining or querying tools that supports semantic information retrieval, extraction and analysis, and (ii) mining of event logs and models at a much more conceptual levels as opposed to the syntactic nature or methods for process mining. The purpose is mainly as a way of providing formal structures for the datasets used for process mining and enhancement of the analysis and integration of the resulting process models. Such an ontology-based approach is significant because, indeed, it involves semantic descriptions and/or reformulation of the meanings of the labels within the event logs and process models, as well as their comparisons for the purpose of improving the usefulness and performance of the entire domain processes in question particularly during the information retrieval, processing, and extraction process. In short, the propose approach in this chapter supports the augmentation of the informative values of the resulting models by semantically annotating the process elements with concepts they represent in real time, and linking them to an ontology in order to allow for analysis of the extracted data logs and models at a much more conceptual level.

In turn, the conceptual method of analysis provides an easy way to analyse the datasets (i.e. the event logs and models), and even more allows the meaning of the process elements to be enhanced through the use of property descriptions languages or syntax—such as the Ontology Web-Rule Language (OWL) [7] Semantic Web Rule Language (SWRL) [8], Description Logic (DL) queries [9], and classification of discoverable entities or taxonomy [4] in order to make available inference knowledge that could be utilized to determine useful patterns by means of the semantic reasoning aptitudes. On the other hand, the semantic modelling (*ontological representations*) and analysis techniques provide us with the opportunity to develop intelligent algorithms and tools which are capable of enhancing the resulting process models through explicit specification of the concepts (often referred to as *conceptualisation*) [5, 10, 11] in order to identify appropriate domain semantics and relationships amongst the process elements.

Finally, the work applies the proposed method on the case study of learning process domain to demonstrate the usefulness of the semantic-based approach. The study takes into consideration the different stages of process mining and its application—from the initial phase of collecting and transformation of the readily available event data to discovered process models, and then to semantically preparing the extracted models for further analysis and process querying at a more abstraction level. In essence, the chapter shows by using the case study of *Learning Process*—how the data from the various process domains can be extracted, semantically prepared, and transformed into mining executable formats to support the discovery, monitoring and enhancement of real-time processes through further semantic analysis of the discovered models. Indeed, the proposals and outcomes of the study shows that a system which is formally encoded with semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner) has the capability to enhance process mining analysis and results from the syntactic level to a much more conceptual level.

Over the following section, the study looks at the ontological concepts and its main functions, and the describe how the work has utilised the schema to develop the proposed semanticbased process mining approach.

## **2. Ontologies**

models and their analysis by means of conceptualization. The conceptual system of analysis allows the meaning of process elements to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge that could

Indeed, the ability to mine useful or worthwhile knowledge from readily extracted data in current information systems is a challenge, due to the exponential increase in volume of data that is continuously generated. Moreover, many of such organizations data collection systems and procedures for the process analysis is proving to be more and more complex. In consequence, this has spanned the need for a richer or advanced description of real-time processes that allows for flexible exploration of the large volumes of data targeted at improving the systems performance and of course the main business operations. Such process-related analysis means there is also need for techniques that are capable of extracting valuable information

More or less, most organization have invested in projects to model their various operational process. However, most of the derived process models are often unfitting, non-operational, or represents a form of reality that are pointed towards comprehensibility rather than covering the entire actual business process complexities. Perhaps, according to the works in Refs. [1–4] an accurate exploration or analysis of the extracted events log is capable of providing vital and valuable information with regards to the quality of support being offered for the so-called organizations and their information knowledge-base or system at large. For example, revealing the underlying relationships the process elements or resources share amongst themselves

Recently, the Process Mining [3] or yet still Process Querying [5] notion has become a valuable technique used to discover such kind of meaningful information from the event data logs and the derived process models. However, the study carried out in [6] observes that a shared challenge with most of the existing process mining techniques is that they depend on tags/labels in event logs information about the processes they represent, and therefore, to a certain extent are limited because they lack the abstraction level required from real world perspectives. This means that the techniques do not technically gain from the real knowledge (semantics) that describe the tags or labels in events log of the domain processes [6]. Practically, majority of the process mining techniques in literature are purely syntactic in nature, and to this effect are

For that reason, this work explores the technological potentials and prospects in using ontology as a core process mining and querying enabling tool by pursuing to address such challenges posed by the lack of semantic information through provision of a method for formal structuring of the readily available datasets. In other words, the work in this chapter addresses the above challenges i.e. (i) lack of process mining or querying tools that supports semantic information retrieval, extraction and analysis, and (ii) mining of event logs and models at a much more conceptual levels as opposed to the syntactic nature or methods for process mining. The purpose is mainly as a way of providing formal structures for the datasets used for process mining and enhancement of the analysis and integration of the resulting process models. Such an ontology-based approach is significant because, indeed, it involves semantic

from the event logs and the resulting models about the real time processes in view.

within the information knowledge-base.

146 Ontology in Information Science

somewhat vague when confronted with unstructured data.

be used to determine useful patterns as well as predict future outcomes.

As a collection of *concepts* and *predicates*, ontology has the ability to perform logic reasoning and bridge the underlying challenges (semantic gaps) beneath event logs and models discovered especially through conventional process mining techniques with rich semantics. To make the semantic knowledge available, ontologies are incorporated with the process models in order to pre-determine the model structure. Besides, the method also serves as a way of representing or bridging the distances between the labels within the process models and concepts in the defined ontologies.

(process descriptions) allows for automatic reasoning of the whole ontology with the aim of retrieving meaningful and useful knowledge that are inferred. Apparently, such reasoning disposition ensures that the process elements specifications within the ontologies are logically interpreted in a suitable manner that enables the automatic reasoning over the explicit

Ontology: Core Process Mining and Querying Enabling Tool

http://dx.doi.org/10.5772/intechopen.71981

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Therefore, the main benefits of ontologies can be summarised in two forms:

or implications of their developed ontologies and design frameworks [18, 20].

**ii.** advanced analysis and reasoning of the processes at a more conceptual levels.

Likewise, one of the main benefits of ontologies particularly the OWL is that the schema is capable of declaring the different *classes* and *object/data properties* in any given process domain. In turn, it classifies those classes or properties into a taxonomy (i.e. *subClass* and *subProperty* hierarchy) by assigning the *domains* and *ranges* in the same way as the RDF schema [7]. Moreover, the resulting logical models allows the use of a *reasoner* to check if or not all of the definitions or expressions within the ontologies are equally consistent and recognises which concepts fits under which class, as well as, what the meaning of the individual specific properties are [19]. To end with, state of the art tools used for constructing ontologies (e.g., Protégé, SWOOP, and TopBraid Composer) makes use of those reasoners to make available the inference knowledge (i.e. the underlying inferred classes) to the developers or users predominantly in understanding the logically impacts

The main benefit of OWL ontologies is the capability to automatically compute the class hierarches (i.e. taxonomy) and the underlying relationships that exist amongst the different process elements (entities) by making use of a *reasoner*. Truly, *Reasoners* [2, 9] are essentially used to infer and check if a specific class is a subClass, or superClass of another, or not at all within

Indeed, an additional function offered by the reasoner especially as used in this study is *consistency checking* of the process elements and parameters. This means that based on the *process description* or *attributes* within the ontology, the reasoner is able to use the underlying information to *check* if it is possible for any instances (individuals) to become a member of a class. Hence, a class is classified as being inconsistent if it cannot perhaps have any instance.

Moreover, a reasoner is every now and then also referred to as *classifier*. According to Ref. [3] a classifier is a function that maps the attributes of an event onto a label used in the resulting process model. Therefore, in context of ontology-based systems, a classifier (i.e. the reasoner) maps the taxonomy of the defined domain process by matching the various classes with their resulting process instances and/or attributes. In short, the process of computing the inferred class hierarchies in an ontology is typically known as *classifying the ontology*. Henceforth, the reasoner is regarded as the *classifier* or the *inference engine* used in querying and manipulation

the ontology, and as such automatically computes the inferred class hierarchy [4, 12].

**i.** encoding knowledge about specific process domains, and

knowledge about the domain processes in view [13].

**3. Semantic reasoning**

of the whole ontology.

Indeed, an ontological schema aims to transforms a process map into a *bipartite graph* (also referred to as Ontograph) to denote both the process models and its elements in a uniformed structure. So, whenever an inference (semantic reasoning) is made, a generalized associations (classification) of the process elements is created, and in consequence, infers the class hierarchies as well as performs a consistency check for those predicates. Besides, the sets of constraints (i.e. Object or Datatype property restrictions) driven by the ontology have the capacity to recognize inconsistent data and outputs particularly during the pre-processing stage, the algorithm executions, filtering or interpretation stage, and the results generation.

Several application and definition of the ontology term has been proposed in literature which most of the time concerns the varied domains of interest. According to Ref. [12] the term ontology is borrowed from the philosophy field which is concerned with being or existence study. The author mentions that in the context of computer and information science, ontology symbolizes as an artefact that is designed to model any domain knowledge of interest.

Even more, Ref. [13] refers to the ontological term as a formal explicit specification of a conceptualisation, and till date has been the most widely cited definition of ontology in the computer field. The definition means that ontology is able to explicitly define (i.e. specifies) concepts and relationships that are pertinent for modelling any domain of interest. Moreover, such specification can be represented in the form of *Classes, Relations, Constraints* and *Rules* to provide more meanings to use of the different expressions or relations. So therefore, ontology performs the following three functions, namely: *Formal—Explicitness—Conceptualisation*—to provide hierarchical structures and representation of information or knowledge.

In principle, ontology helps in description of the various concepts as well as the associations that holds amongst those concepts within a process domain. Hence, ontologies range from taxonomies, classifications, database schemas to fully axiomatized theories which state facts. Moreover, ontologies are nowadays an essential tool to a lot of systems or algorithms that are used for information retrieval and extraction, information management and integration of systems, scientific-knowledge portals, including e-commerce and web services.

Equally, ontology has been broadly used in many other sub-fields of computer science and AI, particularly in areas that concerns Information Retrieval (IR) [14] and Information Extraction (IE) [15], Ontology-Based Information Extraction (OBIE) [16], database management systems [17], information management and intelligent systems integration [18], knowledge representation [19], and in context of this study, Semantic-based Process Mining [2, 4, 6].

Clearly, the representation of knowledge using ontologies helps in organising datasets of complex structures (e.g. the fuzzy models). Moreover, the work in this chapter claims that by using the ontology as a conceptual consistency constraint, a fuzzy model with unlabelled data can be tuned into one (semantic model) that have the best consistency based on the prior knowledge or information. In addition, the formal representations and the resulting metadata (process descriptions) allows for automatic reasoning of the whole ontology with the aim of retrieving meaningful and useful knowledge that are inferred. Apparently, such reasoning disposition ensures that the process elements specifications within the ontologies are logically interpreted in a suitable manner that enables the automatic reasoning over the explicit knowledge about the domain processes in view [13].

Therefore, the main benefits of ontologies can be summarised in two forms:

Likewise, one of the main benefits of ontologies particularly the OWL is that the schema is capable of declaring the different *classes* and *object/data properties* in any given process domain. In turn, it classifies those classes or properties into a taxonomy (i.e. *subClass* and *subProperty* hierarchy) by assigning the *domains* and *ranges* in the same way as the RDF schema [7]. Moreover, the resulting logical models allows the use of a *reasoner* to check if or not all of the definitions or expressions within the ontologies are equally consistent and recognises which concepts fits under which class, as well as, what the meaning of the individual specific properties are [19]. To end with, state of the art tools used for constructing ontologies (e.g., Protégé, SWOOP, and TopBraid Composer) makes use of those reasoners to make available the inference knowledge (i.e. the underlying inferred classes) to the developers or users predominantly in understanding the logically impacts or implications of their developed ontologies and design frameworks [18, 20].

## **3. Semantic reasoning**

To make the semantic knowledge available, ontologies are incorporated with the process models in order to pre-determine the model structure. Besides, the method also serves as a way of representing or bridging the distances between the labels within the process models

Indeed, an ontological schema aims to transforms a process map into a *bipartite graph* (also referred to as Ontograph) to denote both the process models and its elements in a uniformed structure. So, whenever an inference (semantic reasoning) is made, a generalized associations (classification) of the process elements is created, and in consequence, infers the class hierarchies as well as performs a consistency check for those predicates. Besides, the sets of constraints (i.e. Object or Datatype property restrictions) driven by the ontology have the capacity to recognize inconsistent data and outputs particularly during the pre-processing stage, the algorithm executions, filtering or interpretation stage, and the results generation.

Several application and definition of the ontology term has been proposed in literature which most of the time concerns the varied domains of interest. According to Ref. [12] the term ontology is borrowed from the philosophy field which is concerned with being or existence study. The author mentions that in the context of computer and information science, ontology symbolizes as an artefact that is designed to model any domain knowledge of interest.

Even more, Ref. [13] refers to the ontological term as a formal explicit specification of a conceptualisation, and till date has been the most widely cited definition of ontology in the computer field. The definition means that ontology is able to explicitly define (i.e. specifies) concepts and relationships that are pertinent for modelling any domain of interest. Moreover, such specification can be represented in the form of *Classes, Relations, Constraints* and *Rules* to provide more meanings to use of the different expressions or relations. So therefore, ontology performs the following three functions, namely: *Formal—Explicitness—Conceptualisation*—to