Compressing and Querying Skypattern Cubes

Willy Ugarte 1, 2 Samir Loudni 3 Patrice Boizumault 3 Bruno Crémilleux 3 Alexandre Termier 4
3 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
4 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
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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Willy Ugarte, Samir Loudni, Patrice Boizumault, Bruno Crémilleux, Alexandre Termier. Compressing and Querying Skypattern Cubes. IEA/AIE-2019 - 32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, Jul 2019, Graz, Austria. pp.406-421, ⟨10.1007/978-3-030-22999-3_36⟩. ⟨hal-02190788⟩

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