Abstract
In various approaches, data cubes are pre-computed in order to efficiently answer Olap queries. Such cubes are also successfully used for multidimensional analysis of data streams. The notion of data cube has been explored in various ways: iceberg cubes, range cubes, differential cubes or emerging cubes. In this paper, we introduce the concept of convex cube which captures all the tuples satisfying a monotone and/or antimonotone constraint combination. It can be represented in a very compact way in order to optimize both computation time and required storage space. The convex cube is not an additional structure appended to the list of cube variants but we propose it as a unifying structure that we use to characterize, in a simple, sound and homogeneous way, the other quoted types of cubes.
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References
Beyer, K., Ramakrishnan, R.: Bottom-Up Computation of Sparse and Iceberg CUBEs. In: Proceedings of the International Conference on Management of Data, SIGMOD, pp. 359–370 (1999)
Casali, A.: Mining borders of the difference of two datacubes. In: Proceedings of the 6th International Conference on Data Warehousing and Knowledge Discovery, DaWaK, pp. 391–400 (2004)
Casali, A., Cicchetti, R., Lakhal, L.: Cube lattices: a framework for multidimensional data mining. In: Proceedings of the 3rd SIAM International Conference on Data Mining, SDM, pp. 304–308 (2003)
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Mining and Knowledge Discovery 1(1), 29–53 (1997)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)
Han, J., Chen, Y., Dong, G., Pei, J., Wah, B.W., Wang, J., Cai, Y.D.: Stream cube: An architecture for multi-dimensional analysis of data streams. Distributed and Parallel Databases 18(2), 173–197 (2005)
Lakshmanan, L., Pei, J., Han, J.: Quotient cube: How to summarize the semantics of a data cube. In: Proceedings of the 28th International Conference on Very Large Databases, VLDB, pp. 778–789 (2002)
Mitchell, T.M.: Machine learning. Computer Science. MacGraw-Hill, New York (1997)
Nedjar, S., Casali, A., Cicchetti, R., Lakhal, L.: Emerging cubes for trends analysis in Olap databases. In: Proceedings of the 9th International Conference on Data Warehousing and Knowledge Discovery, DaWaK (2007)
Vel, M.: Theory of Convex Structures. North-Holland, Amsterdam (1993)
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Casali, A., Nedjar, S., Cicchetti, R., Lakhal, L. (2007). Convex Cube: Towards a Unified Structure for Multidimensional Databases. In: Wagner, R., Revell, N., Pernul, G. (eds) Database and Expert Systems Applications. DEXA 2007. Lecture Notes in Computer Science, vol 4653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74469-6_56
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DOI: https://doi.org/10.1007/978-3-540-74469-6_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74467-2
Online ISBN: 978-3-540-74469-6
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