International Business Machines Corporation

Christoph H Sieb

Ruben Pulido De Los Reyes

Michael Wurst

Christoph Lingenfelder

Alexander Lang

Bernhard Mitschang

Iliyana P Ivanova

Simone Zerfass

A Business Intelligence (BI) meta model template is selected based on one or more meta model object types in a model structure. A BI meta model is generated from the selected BI meta model template. One or more BI report specification templates are selected based on a mining model type. A BI report specification is generated from the selected one or more BI report specification templates, a schema of the model structure, and content of the model structure. The BI meta model and the BI report specification are deployed to a BI server for use in generating a BI report using a BI tool at the BI server. In response to a user request for a BI report, the BI report is generated with a BI tool at the BI server that uses the BI meta model and the BI report specification.

Deployment of a business intelligence (BI) meta model and a BI report specification for use in presenting data mining and predictive insights using BI tools

Processing data of a data warehouse is provided and includes receiving, by a processing device, user input to create simple filter objects. Each filter object defines an ad hoc subset of a respective dimension of a dimension table of the data warehouse. User input is received to create a filtered operation object that specifies an operation and a plurality of the simple filter objects. The ad hoc subset differs from all subsets defined in the dimension table.

Processing data in a data warehouse

Data of a database environment, which includes hierarchy information and a matrix of values, is processed. The hierarchy information includes at least two sets of identification codes and defines at least two groups of identification codes. The matrix of values includes at least two columns of identification values. At least one simple filter object is generated based on a user input. Each simple filter object defines an ad hoc group of identification codes selected from a respective one of the sets of identification codes. A filtered operation object that specifies an operation and at least one of the simple filter objects is generated based on a user input. Each of the ad hoc groups differs from each of the groups defined by the hierarchy information.

A new data mining model (DMM) is created having at least one of the following characteristics: quality and complexity. The new DMM is handled as a candidate for storing in a storage device if a predefined criterion for the characteristics is met. The sum of the sizes of the new DMM and already stored DMMs is determined. In response to the sum falling below a storage limit, the new DMM is stored in the storage device. In response to the sum exceeding the storage limit, a decision is taken based on priorities of the DMMs which DMMs to store in the storage device.

Selective storing of mining models for enabling interactive data mining

Computerized methods, data processing systems, and computer program products for storing of data mining models (DMMs) are provided. A new DMM is created having at least one of the following characteristics: quality and complexity. The new DMM is handled as a candidate for storing in a storage device if a predefined criterion for the characteristics is met. The sum of the sizes of the new DMM and already stored DMMs is determined. In response to the sum falling below a storage limit, the new DMM is stored in the storage device. In response to the sum exceeding the storage limit, a decision is taken based on priorities of the DMMs which DMMs to store in the storage device. The priorities depend at least on access frequencies of the DMMs. Upon a data mining request, a corresponding DMM is determined and a user is requested to confirm that data mining is to proceed if quality of the determined DMM does not fulfill a further predefined criterion.

A method, system and computer program product for processing data relating to transactions that enables, for example, performing a Drill-Through on a given association rule for a large number of transactions in a short period of time. A Drill-Through (DT) Preparation, which generates the prefix tree and the transaction group information, and the DT Execution, which determines the transactions supporting an association rule, may be carried out in order to extract hidden information from input data.

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Schriesheim, Germany

Christoph H Sieb