Abstract
Multidimensional aggregation plays an important role in decisionmaking systems. A conceptual Multidimensional Aggregation Object (MAO), which consists of measures, scopes and aggregation function, is introduced to represent relationships among aggregators on addressable subsets of data. In the MAO model, aggregations of low-level (intermediate) data can be reused for aggregations on high-level data along the same dimension. Experimental results show that caching intermediate aggregated data can significantly improve performance. Incremental compensating and full recomputing cache-updating approaches are proposed. Execution plans for deriving the aggregations from MAOs are presented. The proposed data aggregation technique can be applied to data-warehousing, OLAP, and data mining tasks.
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References
Albrecht, J., Gunzel, H., Lehner, W.: Set-Derivability of Multidimensional Aggregates. In: DaWak 1999, pp. 133–142 (1999)
Agrawal, S., Agrawal, R., Deshpande, P.M., Gupta, A., Naughton, J.F., Ramakrishnan, R., Sarawagi, S.: On the computation of multidimensional aggregates. In: Proc. 1996 Int. Conf. VLDB 1996, Bombay, India, September 1996, pp. 506–521 (1996)
Agrawal, R., Gupta, A., Sarawagi, S.: Modeling multidimensional databases. In: Proc. 1997 Int. Conf. Data Engineering(ICDE 1997), Birmingham, England, April 1997, pp. 232–243 (1997)
Mumick, I.S., Quass, D., Mumick, B.S.: Maintenance of Data Cubes and Summary Tables in a Warehouse. In: ACM SIGMOD, AZ, USA, pp. 100–111 (1997)
Gray, J., Bosworth, A., Layman, A., Pirahesh, H.: Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals. In: IEEE Data Engineering, pp. 152–159
Shukla, A., Deshpande, P.M., Naughton, J.F., Ramasamy, K.: Storage Estimation for Multidimensional Aggregates in the Presence of Hierarchies. In: VLDB 1996, Mumbai(Bombay), India (1996)
Harinarayan, V., Rajaraman, A., Ullman, J.D.: Implementing Data Cubes Efficiently. In: SIGMOD Conference 1996, pp. 205–216 (1996)
Hellerstein, J.M., Haas, P.J., Wang, H.J.: Online Aggregation. In: SIGMOD Conference 1997, pp. 171–182 (1997)
Han, J., Kamber, M.: Data Mining Concepts and Techniques, pp. 230–243. Morgan Kaufmann Publishers, San Francisco
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© 2003 Springer-Verlag Berlin Heidelberg
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Tsai, MF., Chu, W. (2003). A Multidimensional Aggregation Object (MAO) Framework for Computing Distributive Aggregations. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2003. Lecture Notes in Computer Science, vol 2737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45228-7_6
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DOI: https://doi.org/10.1007/978-3-540-45228-7_6
Publisher Name: Springer, Berlin, Heidelberg
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