On-line analytical processing (OLAP) systems deal with analytical tasks in businesses. As these tasks do not depend on the latest updates by transactions, it is assumed that the data used in OLAP systems are kept in a data warehouse, which separates the input from operational databases from the outputs to OLAP. Typical OLAP queries are data intensive, and thus time consuming. In order to speed up the query computation, it is a common practice to materialize some of the computations as views based on a set of queries given. In general we wish to optimize query time under a given maintenance constraints. However, OLAP queries are not static and may change over time. Thus designing data warehouse is an ongoing task. This process is also called dynamic or incremental design. In this paper, we approach this issue as a refinement step in our Abstract State Machine (ASM) based data warehouse design, and support it by a set of standard refinement rules.
|Cite as: Koehler, H., Schewe, K.-D. and Zhao, J. (2007). Dynamic Data Warehouse Design as a Refinement in ASM-based Approach. In Proc. Fourth Asia-Pacific Conference on Conceptual Modelling (APCCM2007), Ballarat, Australia. CRPIT, 67. Roddick, J. F. and Hinze, A., Eds. ACS. 61-69. |
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