An apparatus for assessing goal attainment may include a preprocessor configured to define a goal of process, all probable final results, attributes, a process execution period, and time points of assessment, acquire or check attribute values from one or more historical process instances that match the defined goal of process, all the defined probable final results, the defined attributes, the defined process execution period, and the defined time point of assessment, and extract one or more event profiles from the attribute values of the process instances; and a likelihood calculator configured to calculate prior and posterior probabilities of an ongoing process instance based on the extracted event profiles, and calculate a probability that the ongoing process instance attains each probable final result according to each time point, using the calculated prior and posterior probabilities at the defined time point of assessment.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus for assessing goal attainment, comprising: a preprocessor configured to define a goal of a process, all probable final results, attributes, a process execution period, and time points of assessment, acquire or check attribute values from one or more historical process instances that match the defined goals of a process, all the defined probable final results, the defined attributes, the defined process execution period, and the defined time points of an assessment, and extract one or more event profiles from the attribute values of the process instances; and a likelihood calculator configured to calculate prior and posterior probabilities of an ongoing process instance based on the extracted event profile(s), and calculate likelihoods that an ongoing process attains each of the probable final results at each time point, using the calculated prior and posterior probabilities at the defined time point of an assessment. 2. The apparatus of claim 1, wherein the attributes of a process instance include at least one or more of an environmental attribute, an object attribute, an activity attribute, and an event attribute. 3. The apparatus of claim 1, wherein the preprocessor creates clusters of the historical process instances by performing cluster analysis based on at least one attribute chosen out of one or more activity attributes and one or more event attributes, and extracts, from each of the created clusters, one or more event profiles, each consisting of attribute values of the historical process instances belonging to each of the clusters. 4. The apparatus of claim 1, wherein the likelihood calculator calculates the prior probability that the ongoing process instance corresponds to each of the extracted event profiles, calculates the posterior probability by applying a likelihood function to the calculated prior probability, and calculates, by applying Bayesian theorem to the calculated prior and posterior probabilities, the likelihoods of the ongoing process instance to attain each probable final result according to each time point. 5. The apparatus of claim 4, wherein the likelihood of the ongoing process instance is calculated using P(Oi|Dt)=Σj=1nP(IεEPj|Dt)P(Oi|IεEPj∩Dt), where Oi denotes an i-th type of probable final result, Dt denotes a set of one or more event values generated by one or more activities of a process instance I until a t-th assessment time point, and EPj denotes a j-th event profile. 6. The apparatus of claim 1, wherein in a case where various process execution periods needed for achieving the goal occur, with respect to each of all possible process execution periods, the likelihood calculator acquires or checks attribute values of historical process instances of each of processes associated with said possible process execution periods and calculates likelihoods of attaining each probable final result that corresponds to each of the process execution periods and a probability of occurrence of each of the process execution periods, and calculates an expected probability that each probable final result occurs, based on the calculated likelihoods of attaining each probable final result and probability of occurrence of each of the process execution periods. 7. The apparatus of claim 6, wherein the expected probability that each probable final result occurs is calculated using ΣL=T∞P(project_length=L|L≧T)*P(Oi,L|Dt,L), where L denotes the process execution period, T denotes a minimum project length, Oi,L denotes the i-th type of probable final result for the process execution period L, and Dt,L denotes a set of one or more event values generated by one or more activities of a process instance I until the t-th assessment time point. 8. The apparatus of claim 1, further comprising: a predictor configured to predict an activity attribute value of the ongoing process instance to be obtained at a future assessment time point, as well as to predict an event attribute value according to the activity, based on current activity attribute values and current event attribute values according to a current activity of the ongoing process instance. 9. The apparatus of claim 8, wherein an event profile type to be used to predict both the activity attribute value and the event attribute value which will be obtained at the future assessment time point is derived using s = argmax j  P  ( I ∈ EP j | D t ) , where EPj denotes a j-th event profile, I denotes an ongoing process instance, Dt denotes a set of one or more event values generated by one or more activities of a process instance I until a t-th assessment time point, and s denotes one of j number of event profiles that creates the highest probability that the process instance I belongs to said event profile when event values obtained until the latest assessment time point t construct a vector Dt. 10. A method for assessing goal attainment, comprising: defining a goal of a process, all probable final results, attributes, a process execution period, and time points of assessment; acquiring or checking attribute values from one or more historical process instances that match the defined goal of a process, all the defined probable final results, the defined attributes, the defined process execution period, and the defined time points of an assessment; extracting one or more event profiles from the attribute values of the historical process instances; calculating prior and posterior probabilities of an ongoing process instance based on the extracted event profile(s); and calculating likelihoods that the ongoing process instance attains each probable final result according to each time point, using the calculated prior and posterior probabilities at the defined time point of assessment. 11. The method of claim 10, wherein the attributes of the process instance include at least one or more of an environmental attribute, an object attribute, an activity attribute, and an event attribute. 12. The method of claim 10, wherein the extracting of the event profiles from the attribute values of the historical process instances comprises creating clusters of the historical process instances by performing cluster analysis based on at least one attribute chosen out of one or more activity attributes and one or more event attributes, and extracting, from each of the created clusters, one or more event profiles, each consisting of attribute values of the historical process instances belonging to each of the clusters. 13. The method of claim 10, wherein the calculating of the prior and posterior probabilities based on the extracted event profiles comprises: calculating the prior probability that the ongoing process instance corresponds to each of the extracted event profiles, and calculating the posterior probability by applying a likelihood function to the calculated prior probability. 14. The method of claim 13, wherein in the calculation of the likelihoods that the ongoing process instance attains each probable final result according to each time point, Bayesian theorem is applied to the calculated prior and posterior probabilities. 15. The method of claim 14, wherein the likelihood of the ongoing process instance is calculated using P(Oi|Dt)=Σj=1nP(IεEPj|Dt)P(Oi|IεEPj∩Dt), where Oi denotes an i-th probable final result, Dt denotes a set of one or more event values generated by one or more activities of a process instance I until a t-th assessment time point, and EPj denotes a j-th event profile. 16. The method of claim 10, further comprising: in a case where various process execution periods are needed for achieving the goal, with respect to each of all possible process execution periods, acquiring or checking attribute values of historical process instances of each of processes associated with said possible process execution periods and calculating likelihoods of attaining each probable final result that corresponds to each of the process execution periods and a probability of occurrence of each of the process execution periods; and calculating an expected probability that each probable final result occurs, based on the calculated likelihoods of attaining each probable final result and probability of occurrence of each of the process execution periods. 17. The method of claim 16, wherein the expected probability that each probable final result occurs is calculated using ΣL=T∞P(project_length=L|L≧T)*P(Oi,L|Dt,L), where L denotes the process execution period, T denotes a minimum project length, Oi,L denotes an i-th type of probable final result for the process execution period L, and Dt,L denotes a set of one or more event values generated by one or more activities of a process instance I until a t-th assessment time point. 18. The method of claim 10, further comprising: predicting an activity attribute value of the ongoing process instance to be obtained at a future assessment time point, as well as predicting the event attribute value according to the activity, based on current activity attribute values and current event attribute values according to one or more current activities of the ongoing process instance. 19. The method of claim 18, wherein an event profile type to be used to predict both the activity attribute value and the event attribute value which will be obtained at the future assessment time point is derived using s = argmax j  P  ( I ∈ EP j | D t ) , where EPj denotes a j-th event profile, I denotes an ongoing process instance, Dt denotes a set of one or more event values generated by one or more activities of a process instance I until a t-th assessment time point, and s denotes one of j number of event profiles creates the highest probability that the process instance I belongs to said event profile when event values obtained until the latest assessment time point t construct a vector Dt.