Abstract: One implementation provides a computer system for invoking execution of analytical tasks in sequence. In this implementation, the computer system is programmed to receive a request to execute an analytical task from a front-end software application, and determine that a first additional analytical task needs to be executed before the requested analytical task. The computer system is then programmed to invoke execution of the first additional analytical task on a first analytical engine. Using information generated from the execution of the first additional analytical task, the computer system is then programmed to invoke execution of the requested analytical task on a second analytical engine.

Abstract: A data analysis workbench enables a user to define a data analysis process that includes an extract sub-process to obtain transactional data from a source system, a load sub-process for providing the extracted data to a data warehouse or data mart, a data mining analysis sub-process to use the obtained transactional data, and a deployment sub-process to make the data mining results accessible by another computer program. Common settings used by each of the sub-processes are defined, as are specialized settings relevant to each of the sub-processes. The invention also enables a user to define an order in which the defined sub-processes are to be executed.

Abstract: An analytical application provider may provide certain middleware functionality that includes updating model output to include textual descriptions of the data mining model and the data fields in a language selected by a front-end application. Certain implementations of the invention relate to a computer-implemented method for providing multi-language support for data mining models. Some implementations relate to computer-implemented method for outputting textual descriptions of data fields in a data mining model in a selected language.

Abstract: One implementation provides a computer system that integrates software applications with analytical software applications. In this implementation, the computer system is programmed to receive a request from a software application to execute an analytical task, such as a prediction task or a key performance indicator (KPI) lookup task. The request includes a task name and input information. The computer system then uses the task name to identify an analytical engine and a data store, and invokes an analytical software application in the analytical engine to execute the analytical task. The analytical task is executed using the identified data store and the received input information. The computer system receives task output from the invoked analytical software application, the task output including output information generated during execution of the analytical task. The computer system then sends the output information to the software application that made the request.

Abstract: A data analysis workbench enables a user to define a data analysis process that includes an extract sub-process to obtain transactional data from a source system, a load sub-process for providing the extracted data to a data warehouse or data mart, a data mining analysis sub-process to use the obtained transactional data, and a deployment sub-process to make the data mining results accessible by another computer program. Common settings used by each of the sub-processes are defined, as are specialized settings relevant to each of the sub-processes. The invention also enables a user to define an order in which the defined sub-processes are to be executed. The defined data analysis process then is able to be performed by one or more computer systems.

Abstract: One implementation provides a computer system that integrates software applications with analytical software applications. In this implementation, the computer system is programmed to receive a request from a software application to execute an analytical task, such as a prediction task or a key performance indicator (KPI) lookup task. The request includes a task name and input information. The computer system then uses the task name to identify an analytical engine and a data store, and invokes an analytical software application in the analytical engine to execute the analytical task. The analytical task is executed using the identified data store and the received input information. The computer system receives task output from the invoked analytical software application, the task output including output information generated during execution of the analytical task. The computer system then sends the output information to the software application that made the request.

Abstract: One implementation provides a computer system for invoking execution of analytical tasks in sequence. In this implementation, the computer system is programmed to receive a request to execute an analytical task from a front-end software application, and determine that a first additional analytical task needs to be executed before the requested analytical task. The computer system is then programmed to invoke execution of the first additional analytical task on a first analytical engine. Using information generated from the execution of the first additional analytical task, the computer system is then programmed to invoke execution of the requested analytical task on a second analytical engine.

Abstract: An analytical application provider may provide certain middleware functionality that includes updating model output to include textual descriptions of the data mining model and the data fields in a language selected by a front-end application. Certain implementations of the invention relate to a computer-implemented method for providing multi-language support for data mining models. Some implementations relate to computer-implemented method for outputting textual descriptions of data fields in a data mining model in a selected language.

Abstract: A data mining run that includes special analyses is triggered directly after having loaded new data in a data warehouse environment, to enrich the newly loaded data by new attributes. The process automates replicating transaction data from a source system into a data warehouse, triggering a data mining procedure (such as a training or a prediction procedure) that enriches the data with new attributes, and triggering the upload of the enriched data back into the data warehouse.