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Abstract
Applications like Online Analytical Processing depend heavily on the ability to quickly summarize large amounts of information. Techniques were proposed recently that speed up aggregate range queries on MOLAP data cubes by storing pre-computed aggregates. These approaches try to handle data cubes of any dimensionality by dealing with all dimensions at the same time and treat the different dimensions uniformly. The algorithms are typically complex, and it is difficult to prove their correctness and to analyze their performance. We present a new technique to generate Iterative Data Cubes (IDC) that addresses these problems. The proposed approach provides a modular framework for combining one-dimensional aggregation techniques to create space-optimal high-dimensional data cubes. A large variety of cost tradeoffs for high-dimensional IDC can be generated, making it easy to find the right configuration based on the application requirements.
This work was partially supported by NSF grants EIA-9818320, IIS-98-17432, and IIS-99-70700.
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Authors and Affiliations
Dept. of Computer Science, University of California, CA 93106, Santa Barbara, USA
Mirek Riedewald, Divyakant Agrawal & Amr El Abbadi
- Mirek Riedewald
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- Divyakant Agrawal
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- Amr El Abbadi
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Editors and Affiliations
Limburg University (LUC), 3590, Diepenbeek, Belgium
Jan Van den Bussche
Department of Computer Science and Engineering, University of California, 92093-0114, La Jolla, CA, USA
Victor Vianu
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Riedewald, M., Agrawal, D., El Abbadi, A. (2001). Flexible Data Cubes for Online Aggregation. In: Van den Bussche, J., Vianu, V. (eds) Database Theory — ICDT 2001. ICDT 2001. Lecture Notes in Computer Science, vol 1973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44503-X_11
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