Titlebook: Data Warehousing and Knowledge Discovery; 9th International Co Il Yeal Song,Johann Eder,Tho Manh Nguyen Conference proceedings 2007 Springe

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OLAP Technology for Business Process Intelligence: Challenges and Solutionsousing and mining technologies. However, the differences in the underlying assumptions and objectives of the business process model and the multidimensional data model aggravate a straightforward solution for a meaningful convergence of the two concepts..This paper presents the results of an ongoing

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Built-In Indicators to Automatically Detect Interesting Cells in a Cubehile exploring the cube, analysts are rapidly confronted by analyzing a huge number of visible cells to identify the most interesting ones. Coupling OLAP technologies and mining methods may help them by the automation of this tedious task. In the scope of discovery-driven exploration, this paper pre

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Integrating Clustering Data Mining into the Multidimensional Modeling of Data Warehouses with UML PrDWs) can help users to analyze stored data, because they contain preprocessed data for analysis purposes. Furthermore, the . (MD) model of DWs, intuitively represents the system underneath. However, most of the clustering data mining are applied at a low-level of abstraction to complex unstructured

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A UML Profile for Representing Business Object States in a Data Warehousested in the states of these business objects: A customer is either a potential customer, a first time customer, a regular customer or a past customer; purchase orders may be pending or fullfilled..Business objects and their states can be distributed over many parts of the DWH, and appear in measures

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