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    188宝金博页面版: Maintaining Temporal Warehouse Models

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    内容提示： Maintaining Temporal Warehouse Models Johaiin Eder ', Christian Koncilia ^, and Karl Wiggisser ^ 1 University of Vienna, Dept. of Knowledge and Business Engineering Rathausstrasse 19, 1010 Wien, Austria johann.eder@univie.ac.at 2 Panoratio Database Images, Inc. Theresienstrasse 4, 80333 Muenchen, Germany christian.koncilia@panoratio.de 3 University of Klagenftirt, Dept of Informatics-Systems Universitaetsstrasse 65-67, 9020 Klagenftirt, Austria karl.wiggisser@isys.uni-klu.ac.at Abstract. DWT is a tool f...

    文档格式：PDF | 页数：10 | 浏览次数：39 | 上传日期：2018-03-30 07:38:43 | 文档星级：
    Maintaining Temporal Warehouse Models Johaiin Eder ', Christian Koncilia ^, and Karl Wiggisser ^ 1 University of Vienna, Dept. of Knowledge and Business Engineering Rathausstrasse 19, 1010 Wien, Austria johann.eder@univie.ac.at 2 Panoratio Database Images, Inc. Theresienstrasse 4, 80333 Muenchen, Germany christian.koncilia@panoratio.de 3 University of Klagenftirt, Dept of Informatics-Systems Universitaetsstrasse 65-67, 9020 Klagenftirt, Austria karl.wiggisser@isys.uni-klu.ac.at Abstract. DWT is a tool for the maintenance of data warehouse structures based on the temporal data warehouse model COMET. Data warehouse systems do not provide support for maintaining changes in dimension data. DWT allows keeping track of modifications made in the dimension-structure of multidimensional cubes stored in an OLAP (On-Line Analytical Processing) system. We present the overall structure of the DWT system, which allows to upload and download warehouse models in different modeling notations in a time conscious manner, load edit scripts describing changes between versions of warehouse models and apply these edit scripts. We present the workflows for maintenance of warehouse models and discuss how maintenance can be supported with the various integrated tools of DWT . 1 Introduction Data Warehouses are integrated materialized collections of data typically from different heterogeneous data sources. They provide sophisticated support for aggregating, analyzing and comparing data to support decision making. The most popular architecture for data warehouses is the multidimensional datamodel, where transaction data (also called cells or fact data) is described in terms of masterdata (also called dimension members). Usually, members are hierarchically organized in dimensions. Data warehouses are well prepared to deal with modifications in transaction data, e.g. the changing values of the fact Turnover over the time can be covered by introducing a dimension Time. Not surprisingly, most multidimensional models Please use the following format when citing this chapter: Eder, J., Koncilia, C, Wiggisser, K., 2006, in International Federation for Information Processing, Volume 205, Research and Practical Issues of Enterprise Information Systems, eds. Tjoa, A.M., Xu, L., Chaudhry, S., (Boston:Springer), pp.21-30.

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