Abstract
In this chapter, we investigate reduced representations for the Constrained Cube. We use the borders, classical in data mining, for the Constrained Cube. These borders are the boundaries of the solution space and can support classification tasks. However, the borders do not make possible to retrieve the measure values and therefore cannot be used to answer Olap queries. This is why we introduce two new and reduced representations without measure loss: the Constrained Closed Cube and Constrained Quotient Cube. The former representation is based on the concept of cube closure. It is one of the smallest possible representations of cubes. Provided with the Constrained Closed Cube and thus by storing the minimal information, it is possible to answer efficiently queries which can be answered from the Constrained Cube itself. The latter representation is supported by the structure of the Quotient Cube which was proposed to summarize data cubes. The Quotient Cube is revisited in order to provide it with a closure-based semantics and thus adapt it to the context of the Constrained Cube. The resulting Constrained Quotient Cube is less reduced than the Constrained Closed Cube but it preserves the “specialization / generalization” property of the Quotient Cube which makes it possible to navigate within the Constrained Cube. We also state the relationship between the two introduced representations and the one based on the borders. Experiments performed on various data sets are intended to measure the size of the three representations. As expected in the most common situations (real data), the space reduction for each representation is significant comparatively to the size of the Constrained Cube. Thus depending on the user future needs, each of the proposed representations supplies a significant space reduction for a specific use: Borders for Olap classification, Constrained Closed Cube for Olap querying and Constrained Quotient Cube for cube navigation.
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References
Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., Lakhal, L.: Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets. In: CL 2000. LNCS (LNAI), vol. 1861, pp. 972–986. Springer, Heidelberg (2000)
Beyer, K.S., Ramakrishnan, R.: Bottom-up computation of sparse and iceberg cubes. In: Delis, A., Faloutsos, C., Ghandeharizadeh, S. (eds.) SIGMOD Conference, pp. 359–370. ACM Press (1999)
Bonchi, F., Lucchese, C.: On closed constrained frequent pattern mining. In: Morik, K., Rastogi, R. (eds.) ICDM, pp. 35–42. IEEE Computer Society (2004)
Casali, A.: Mining Borders of the Difference of Two Datacubes. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2004. LNCS, vol. 3181, pp. 391–400. Springer, Heidelberg (2004)
Casali, A., Cicchetti, R., Lakhal, L.: Cube lattices: A framework for multidimensional data mining. In: Barbará, D., Kamath, C. (eds.) SDM. SIAM (2003a)
Casali, A., Cicchetti, R., Lakhal, L.: Extracting semantics from data cubes using cube transversals and closures. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) KDD, pp. 69–78. ACM (2003b)
Casali, A., Nedjar, S., Cicchetti, R., Lakhal, L.: Convex Cube: Towards a Unified Structure for Multidimensional Databases. In: Wagner, R., Revell, N., Pernul, G. (eds.) DEXA 2007. LNCS, vol. 4653, pp. 572–581. Springer, Heidelberg (2007)
Casali, A., Nedjar, S., Cicchetti, R., Lakhal, L.: Closed cube lattices. Annals of Information Systems 3(1), 145–164 (2009a); New Trends in Data Warehousing and Data Analysis
Casali, A., Nedjar, S., Cicchetti, R., Lakhal, L., Novelli, N.: Lossless reduction of datacubes using partitions. IJDWM 5(1), 18–35 (2009b)
Dong, G., Li, J.: Mining border descriptions of emerging patterns from dataset pairs. Knowl. Inf. Syst. 8(2), 178–202 (2005)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)
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 Min. Knowl. Discov. 1(1), 29–53 (1997)
Harinarayan, V., Rajaraman, A., Ullman, J.D.: Implementing data cubes efficiently. In: Jagadish, H.V., Mumick, I.S. (eds.) SIGMOD Conference, pp. 205–216. ACM Press (1996)
Lakshmanan, L.V.S., Pei, J., Han, J.: Quotient cube: How to summarize the semantics of a data cube. In: Lochovsky, F.H., Shan, W. (eds.) VLDB, pp. 778–789. Morgan Kaufmann (2002)
Morfonios, K., Ioannidis, Y.E.: Cure for cubes: Cubing using a rolap engine. In: Dayal, U., Whang, K.-Y., Lomet, D.B., Alonso, G., Lohman, G.M., Kersten, M.L., Cha, S.K., Kim, Y.-K. (eds.) VLDB, pp. 379–390. ACM (2006)
Nedjar, S.: Cubes Émergents pour l’analyse des renversements de tendances dans les bases de données multidimensionnelles. PhD thesis. Université de Méditerranée (2009)
Nedjar, S., Casali, A., Cicchetti, R., Lakhal, L.: Emerging Cubes for Trends Analysis in olap Databases. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 135–144. Springer, Heidelberg (2007)
Nedjar, S., Casali, A., Cicchetti, R., Lakhal, L.: Emerging cubes: Borders, size estimations and lossless reductions. Information Systems 34(6), 536–550 (2009)
Nedjar, S., Casali, A., Cicchetti, R., Lakhal, L.: Constrained Closed Datacubes. In: Kwuida, L., Sertkaya, B. (eds.) ICFCA 2010. LNCS, vol. 5986, pp. 177–192. Springer, Heidelberg (2010a)
Nedjar, S., Casali, A., Cicchetti, R., Lakhal, L.: Cubes fermés / quotients émergents. In: EGC 2010 – Extraction et Gestion des Connaissances. Revue des Nouvelles Technologies de l’Information, vol. RNTI-E-19, pp. 285–296. Cépaduès-Éditions (2010b)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient mining of association rules using closed itemset lattices. Information Systems 24(1), 25–46 (1999)
Pei, J., Han, J., Lakshmanan, L.V.S.: Pushing convertible constraints in frequent itemset mining. Data Min. Knowl. Discov. 8(3), 227–252 (2004)
Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing iceberg concept lattices with titanic. Data Knowl. Eng. 42(2), 189–222 (2002)
Xin, D., Han, J., Li, X., Shao, Z., Wah, B.W.: Computing iceberg cubes by top-down and bottom-up integration: The starcubing approach. IEEE Trans. Knowl. Data Eng. 19(1), 111–126 (2007)
Xin, D., Shao, Z., Han, J., Liu, H.: C-cubing: Efficient computation of closed cubes by aggregation-based checking. In: Liu, L., Reuter, A., Whang, K.-Y., Zhang, J. (eds.) ICDE, p. 4. IEEE Computer Society (2006)
Zaki, M.J.: Generating non-redundant association rules. In: KDD, pp. 34–43 (2000)
Zhang, X., Chou, P.L., Dong, G.: Efficient computation of iceberg cubes by bounding aggregate functions. IEEE Trans. Knowl. Data Eng. 19(7), 903–918 (2007)
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Cicchetti, R., Lakhal, L., Nedjar, S. (2012). Constrained Closed and Quotient Cubes. In: Guillet, F., Ritschard, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25838-1_1
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DOI: https://doi.org/10.1007/978-3-642-25838-1_1
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