Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.