Abstract
Skypatterns are important since they enable to take into account user preference through Pareto-dominance. Given a set of measures, a skypattern query finds the patterns that are not dominated by others. In practice, different users may be interested in different measures, and issue queries on any subset of measures (a.k.a subspace). This issue was recently addressed by introducing the concept of skypattern cubes. However, such a structure presents high redundancy and is not well adapted for updating operations like adding or removing measures, due to the high costs of subspace computations in retrieving skypatterns. In this paper, we propose a new structure called Compressed Skypattern Cube (abbreviated CSKYC), which concisely represents a skypattern cube, and gives an efficient algorithm to compute it. We thoroughly explore its properties and provide an efficient query processing algorithm. Experimental results show that our proposal allows to construct and to query a CSKYC very efficiently.
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Bøgh, K.S., Chester, S., Sidlauskas, D., Assent, I.: Hashcube: a data structure for space- and query-efficient skycube compression. In: CIKM, pp. 1767–1770 (2014)
Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE, pp. 421–430 (2001)
Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: ICDE, pp. 717–719 (2003)
Dua, D., Graff, C.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml
Hansen, K., Mika, S., Schroeter, T., Sutter, A., ter Laak, A., Steger-Hartmann, T., Heinrich, N., Müller, K.: Benchmark data set for in silico prediction of Ames mutagenicity. JCIM 49(9), 2077–2081 (2009)
Hanusse, N., Kamnang Wanko, K., Maabout, S.: Computing and summarizing the negative skycube. In: CIKM, pp. 1733–1742 (2016)
Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Min. Knowl. Discov. 1(3), 241–258 (1997)
Pei, J., Fu, A.W., Lin, X., Wang, H.: Computing compressed multidimensional skyline cubes efficiently. In: ICDE, pp. 96–105 (2007)
Pei, J., Jin, W., Ester, M., Tao, Y.: Catching the best views of skyline: a semantic approach based on decisive subspaces. In: VLDB, pp. 253–264 (2005)
Pham, H., Lavenier, D., Termier, A.: Identifying genetic variant combinations using skypatterns. In: DEXA Workshops, pp. 44–48. IEEE Computer Society (2016)
Soulet, A., Raïssi, C., Plantevit, M., Crémilleux, B.: Mining dominant patterns in the sky. In: ICDM, pp. 655–664 (2011)
Ugarte, W., et al.: Skypattern mining: from pattern condensed representations to dynamic constraint satisfaction problems. Artif. Intell. 244, 48–69 (2017)
Ugarte, W., Boizumault, P., Loudni, S., Crémilleux, B.: Computing skypattern cubes. In: ECAI, pp. 903–908 (2014)
Ugarte, W., Boizumault, P., Loudni, S., Crémilleux, B.: Computing skypattern cubes using relaxation. In: ICTAI, pp. 859–866 (2014)
Ugarte Rojas, W., Boizumault, P., Loudni, S., Crémilleux, B., Lepailleur, A.: Mining (Soft-) Skypatterns using dynamic CSP. In: Simonis, H. (ed.) CPAIOR 2014. LNCS, vol. 8451, pp. 71–87. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07046-9_6
Xia, T., Zhang, D.: Refreshing the sky: the compressed skycube with efficient support for frequent updates. In: SIGMOD Conference, pp. 491–502 (2006)
Yuan, Y., Lin, X., Liu, Q., Wang, W., Yu, J.X., Zhang, Q.: Efficient computation of the skyline cube. In: VLDB, pp. 241–252 (2005)
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Ugarte, W., Loudni, S., Boizumault, P., Crémilleux, B., Termier, A. (2019). Compressing and Querying Skypattern Cubes. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_36
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DOI: https://doi.org/10.1007/978-3-030-22999-3_36
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