Examples relate to systems for authoring and executing predictive models. A computer system includes a model development context analyzer configured to store a set of derived modeling knowledge generated at least in part from a plurality of modeling operations performed using at least a first predictive model authoring tool. The system is configured to, receive a modeling context indicating at least a modeling operation being performed, determine, from the modeling context, at least one element of an ontology, the ontology defining at least one attribute of a plurality of modeling operations, query the set of derived modeling knowledge using the at least one element of the ontology to identify at least one record of the set of derived modeling knowledge associated with the at least one element of the ontology, identify at least one suggested model parameter associated with the modeling context, and provide the at least one suggested model parameter.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system implementing a model development context analyzer for generating predictive models, the computer system comprising: the model development context analyzer configured to: store a set of derived modeling knowledge generated at least in part from a plurality of modeling operations performed using at least a first predictive model authoring tool; receive, from the first predictive model authoring tool or a second predictive model authoring tool, a modeling context indicating at least a modeling operation being performed using the first predictive model authoring tool or the second predictive model authoring tool; determine, from the modeling context, at least one element of an ontology, the ontology defining at least one attribute of a plurality of modeling operations; query the set of derived modeling knowledge using the at least one element of the ontology to identify at least one record of the set of derived modeling knowledge associated with the at least one element of the ontology; process the at least one record to identify at least one suggested model parameter associated with the modeling context; and provide the at least one suggested model parameter to the first predictive model authoring tool or the second predictive model authoring tool from which the modeling context was received. 2. The computer system of claim 1, wherein the at least one record comprises a plurality of records and wherein processing the at least one record further comprises: determining a first frequency of a first particular model parameter among the plurality of records; and selecting the first particular model parameter as the at least one suggested model parameter based at least in part on the frequency. 3. The computer system of claim 2, wherein processing the at least one record further comprises: determining a second frequency of a second particular model parameter among the plurality of records; and determining that the first frequency is greater than the second frequency, wherein the first particular model parameter is selected as the suggested model parameter based at least in part on the first frequency being greater than the second frequency. 4. The computer system of claim 1, wherein the at least one record comprises a plurality of records and wherein the computer system is further configured to: identify a plurality of predictive models associated with the plurality of records; retrieve, from an asset datastore, information related to performance of the plurality of predictive models; select a subset of records of the plurality of records for analysis based on the information related to performance of the plurality of predictive models; and select the suggested model parameter from the subset of records. 5. The computer system of claim 4, wherein the information related to performance of the plurality of predictive models comprises an error rate of each of the plurality of predictive models. 6. The computer system of claim 5, wherein the error rate is calculated by determining a ratio between a rate of a predicted occurrence of an event predicted by the predictive model and a rate of an actual occurrence of the event as measured by at least one sensor. 7. The computer system of claim 1, wherein the modeling context comprises a text string entered into a search field of a predictive model authoring tool. 8. The computer system of claim 1, wherein the set of derived modeling knowledge is stored as a Resource Description Framework datastore and wherein the ontology forms a schema for the set of derived modeling knowledge. 9. The computer system of claim 1, wherein the modeling context comprises at least one of a user account identifier, an organization identifier, an asset type, or a modeling technique. 10. The computer system of claim 1, wherein providing the at least one suggested model parameter further comprises populating at least one interface control of the first predictive model authoring tool or the second predictive model authoring tool with the at least one suggested model parameter. 11. A computer-implemented method for programmatically determining modeling parameters for predictive models using a model development context analyzer in communication with a predictive model authoring integrated development environment, the method comprising: storing a set of derived modeling knowledge generated at least in part from a plurality of modeling operations performed using at least a first predictive model authoring tool; receiving, from the first predictive model authoring tool or a second predictive model authoring tool, a modeling context indicating at least a modeling operation being performed using the first predictive model authoring tool or the second predictive model authoring tool; determining, from the modeling context, at least one element of an ontology, the ontology defining at least one attribute of a plurality of modeling operations; querying the set of derived modeling knowledge using the at least one element of the ontology to identify at least one record of the set of derived modeling knowledge associated with the at least one element of the ontology; processing the at least one record to identify at least one suggested model parameter associated with the modeling context; and providing the at least one suggested model parameter to the first predictive model authoring tool or the second predictive model authoring tool from which the modeling context was received. 12. The method of claim 11, wherein the at least one record comprises a plurality of records and wherein processing the at least one record further comprises: determining a first frequency of a first particular model parameter among the plurality of records; and selecting the first particular model parameter as the at least one suggested model parameter based at least in part on the frequency. 13. The method of claim 12, wherein processing the at least one record further comprises: determining a second frequency of a second particular model parameter among the plurality of records; and determining that the first frequency is greater than the second frequency, wherein the first particular model parameter is selected as the suggested model parameter based at least in part on the first frequency being greater than the second frequency. 14. The method of claim 1, wherein the at least one record comprises a plurality of records and wherein the method further comprises: identifying a plurality of predictive models associated with the plurality of records; retrieving, from an asset datastore, information related to performance of the plurality of predictive models; selecting a subset of records of the plurality of records for analysis based on the information related to performance of the plurality of predictive models; and selecting the suggested model parameter from the subset of records. 15. The method of claim 14, wherein the information related to performance of the plurality of predictive models comprises an error rate of each of the plurality of predictive models. 16. The method of claim 15, wherein the error rate is calculated by determining a ratio between a rate of a predicted occurrence of an event predicted by the predictive model and a rate of an actual occurrence of the event as measured by at least one sensor. 17. The method of claim 11, wherein the modeling context comprises a text string entered into a search field of a predictive model authoring tool. 18. The method of claim 11, wherein the modeling context comprises at least one of a user account identifier, an organization identifier, an asset type, or a modeling technique. 19. A computer system implementing a development environment for generating predictive models using model parameters provided by a predictive model authoring tool, the computer system comprising: the predictive model authoring tool configured to: perform a modeling operation based on one or more user inputs provided to interface controls of the predictive model authoring tool; determine a modeling context for the modeling operation; provide the modeling context to a model development context analyzer; receive, from the model development context analyzer, a set of model parameters determined at least in part based on the modeling context; generate a predictive model using at least the set of model parameters received from the model development context analyzer; link the predictive model to an asset, such that one or more sets of data received from the asset are provided to the predictive model during execution of the predictive model; and cause the predictive model to be executed such that the predictive model receives data from the asset, wherein the set of model parameters configure at least one aspect of the execution of the predictive model. 20. The computer system of claim 9, wherein the computer system is further configured to determine the modeling context by receiving text input to a search field.