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 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: 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: 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: 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: 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: Methods, systems, and computer program products for programmatically serializing complex objects (such as JavaBeans™). In contrast to prior art techniques, a manually-specified data type mapping specification is not required as input to the serialization process. Instead, the present invention programmatically generates this type mapping, responsive to encountering run-time exceptions during the serialization process. The serialization process is therefore “self-healing”. This approach is especially advantageous when serializing complex objects that may include complex data types and/or embedded objects. The programmatically-generated type mapping information is preferably used to generate a serialized version of the complex object, and can also be used during deserialization. In addition to improving serialization techniques, this type mapping information facilitates dynamic integration and transformation of data between heterogeneous formats.

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: 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: 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: 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: Methods, systems, and computer program products for programmatically serializing complex objects (such as JavaBeans™). In contrast to prior art techniques, a manually-specified data type mapping specification is not required as input to the serialization process. Instead, the present invention programmatically generates this type mapping, responsive to encountering run-time exceptions during the serialization process. The serialization process is therefore “self-healing”. This approach is especially advantageous when serializing complex objects that may include complex data types and/or embedded objects. The programmatically-generated type mapping information is preferably used to generate a serialized version of the complex object, and can also be used during deserialization. In addition to improving serialization techniques, this type mapping information facilitates dynamic integration and transformation of data between heterogeneous formats.