Abstract: A method to identify database triggers in a data processing system. A request is received to create a database monitor for a specific application event and in response to receiving the request to create the database monitor, the database monitor is created. Then, a request is received to monitor the specific application event within a database and in response to receiving the request to monitor the specific application event, a replicate database of the database is created. Subsequently, the database is compared to the replicate database after the specific application event occurs to identify changes in the database associated with the specific application event. A best candidate is identified for a database trigger based upon the identified changes in the database associated with the specific application event.

Abstract: A system to identify database triggers in a data processing system. A request is received to create a database monitor for a specific application event and in response to receiving the request to create the database monitor, the database monitor is created. Then, a request is received to monitor the specific application event within a database and in response to receiving the request to monitor the specific application event, a replicate database of the database is created. Subsequently, the database is compared to the replicate database after the specific application event occurs to identify changes in the database associated with the specific application event. A best candidate is identified for a database trigger based upon the identified changes in the database associated with the specific application event.

Abstract: A data warehouse is generated by incorporating data warehouse information in business objects to provide subscribed business objects and generating star-schema tables of the data warehouse from the subscribed business objects. Data from subscribed business objects may be logged when an event of the subscribed business objects is processed, for example, by an integration node, and the logged data incorporated into the star-schema tables of the data warehouse.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: A system to identify database triggers in a data processing system. A request is received to create a database monitor for a specific application event and in response to receiving the request to create the database monitor, the database monitor is created. Then, a request is received to monitor the specific application event within a database and in response to receiving the request to monitor the specific application event, a replicate database of the database is created. Subsequently, the database is compared to the replicate database after the specific application event occurs to identify changes in the database associated with the specific application event. A best candidate is identified for a database trigger based upon the identified changes in the database associated with the specific application event.

Abstract: Accessing an analytical model is provided by invoking the analytical model hosted by an analytic engine through a web services interface to the analytic engine. Invocation of the analytical model through the web services interface may be independent of the analytic engine hosting the analytical model. Furthermore, the analytical model may be a predictive model markup language (PMML) model. Invoking the analytical model may be provided by creating a set of tables utilized to store model information and parsing a PMML modeling language representation of the analytical model to populate the set of tables. A web services signature is generated for the analytical model based on the populated set of tables.

Abstract: An improved solution for evaluating one or more data mining algorithms. A set of goals for the data mining algorithm(s) is obtained, and a weight can be assigned to each goal. Each data mining algorithm is applied to a dataset to generate a set of results. A performance value for each data mining algorithm can be calculated based on the weights and set of results. When multiple data mining algorithms are being evaluated, their respective performances can be compared using the respective sets of results and performance values.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: Under the present invention, a data exploration system, a customized model system and an existing model system are provided. The data exploration system analyzes user data to identify statistical information such as data distribution, data relationships, data outliners and invalid or missing data values. The customized model center iteratively generates customized data mining models in parallel based on permutations of the user data, user-provided business parameters and/or a set of model generation algorithms. The existing model system provides users with a library of existing data mining models, assembled based on the business parameters, from which they can choose one or more. In any event, any customized or existing data mining models selected can be run against the user data in parallel.

Abstract: Under the present invention, columns of the user data schema are first matched to corresponding columns of the mining model schema. Once the columns are matched, it will be determined whether data within matching columns of the user data schema has a data type different than data within the corresponding columns of the mining model schema. If so, the data within the matching columns of the user data schema is transformed to match the data type of the data within the corresponding columns of the mining model schema. After any transformation is performed, the user/operator is provided with an opportunity to alter or override the mapping. Once the final mapping is provided, one or more matching resources can be updated to reflect the mapping.

Abstract: Accessing an analytical model is provided by invoking the analytical model hosted by an analytic engine through a web services interface to the analytic engine. Invocation of the analytical model through the web services interface may be independent of the analytic engine hosting the analytical model. Furthermore, the analytical model may be a predictive model markup language (PMML) model. Invoking the analytical model may be provided by creating a set of tables utilized to store model information and parsing a PMML modeling language representation of the analytical model to populate the set of tables. A web services signature is generated for the analytical model based on the populated set of tables.

Abstract: Computations included in analytics of a multi-dimensional cube are generated by analyzing a spreadsheet corresponding to data downloaded from the multi-dimensional cube so as to automatically convert a formula utilizing the downloaded data contained in the spreadsheet into a language of the multi-dimensional cube so as to provide a converted formula. The converted formula is incorporated into the multi-dimensional cube as a computed member.

Abstract: A data warehouse is generated by incorporating data warehouse information in business objects to provide subscribed business objects and generating star-schema tables of the data warehouse from the subscribed business objects. Data from subscribed business objects may be logged when an event of the subscribed business objects is processed, for example, by an integration node, and the logged data incorporated into the star-schema tables of the data warehouse.

Abstract: A common set of building block action modules perform specific tasks in the finite state machine and are strongly modular in structure. The set of building block action modules can be made up of modules for tasks generic to resource type and modules that are resource type independent. A finite state machine is created for each resource type to govern the steps of activation and deactivation of the resource. Each finite state machine, uniquely defines the new state and action processing for each resource type. To tie the building block action modules to each finite state machine, action vectors are created for each resource type. The action vector correlates a particular action selection by the finite state machine to the dispatching of one or more building block action modules. An action vector can contain a plurality of elements. Each of these elements identifies an action module to which control is passed and a function request pointer.