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- W2410631239 abstract "Data warehouses provide sophisticated features for aggregating, analyzing, and comparing data to support decision making in companies. The most popular architecture for data warehouses are multidimensional data cubes, where transaction data (called cells, fact data or measures) are described in terms of master data (also called dimension members). Master data is hierarchically organized in dimensions, where the facts of the upper levels are computed from the facts of the lower levels by some consolidation functions. Available OLAP (On-Line Analytical Processing) systems are therefore able to deal with changing measures, e.g., changing profit or turnover. Surprisingly, they are not able to deal with modifications in dimensions, e.g., if a new branch or division is established, although time is usually explicitly represented as a dimension in data warehouses. The COMET approach is a data warehouse metamodel which allows to represent not only changes of transaction data, but also of schema, and structure data. The COMET model can then be used as basis of OLAP tools which are aware of structural changes and permit correct query results spanning multiple periods and thus different versions of dimension data. This thesis consists not only of a formal metamodel, but also of an implementation of the COMET approach. This implementation serves as a layer between the data warehouse sources and a standard front-end like Hyperion Essbase, Oracle Express or Cognos PowerPlay. Therefore, it can be easily integrated into an existing data warehouse architecture." @default.
- W2410631239 created "2016-06-24" @default.
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- W2410631239 date "2003-01-01" @default.
- W2410631239 modified "2023-09-26" @default.
- W2410631239 title "The comet temporal data warehouse" @default.
- W2410631239 hasPublicationYear "2003" @default.
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