Computing Skypattern Cubes Using Relaxation

Ugarte, Willy; Boizumault, Patrice; Loudni, Samir; Crémilleux, Bruno

doi:10.1109/ictai.2014.132

Cited by 7 publications

(10 citation statements)

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“…Furthermore, for a space M there are 2 |M| −1 different skypattern sets. The Skypattern Cube [13] retrieves all skypattern sets for any subspace. For computing the skypattern cube, [13] has proposed a bottom-up approach using two derivation rules that provide an easy way to automatically infer a large proportion of the skypatterns of a parent node from the skypattern sets of its child nodes without any dominance test (if k measures are associated to a parent node, its child nodes are the nodes defined by the k k−1 subsets of k − 1 measures).…”

Section: Definition 2 (Skypattern Operator)mentioning

confidence: 99%

“…The authors in [13] showed that incomparable skypatterns and some indistinct skypatterns of a child subspace remain also skypatterns in its parent subspace (they are referred to as derivable skypatterns). They also showed that a parent subspace can include nonderivable skypatterns (i.e., those that are not skypatterns in any of its child subspaces).…”

Section: Computing the Cskycmentioning

confidence: 99%

“…This issue has been first addressed with the notion of a Skypattern Cube [13], which is the lattice of all possible subsets of measures associated with their skypattern sets. More formally, given a set M of n measures, the 2 n −1 possible non-empty skypattern subsets should be precomputed to efficiently handle various queries of users.…”

Section: Introductionmentioning

confidence: 99%

“…To sum up, the cube is a structure that enables to discover the most interesting skypattern sets. The skypattern cube has been exploited in various domains such as bioinformatics [10] and mutagenicity [13]. However there are 2 n −1 possible non-empty skypattern sets with high redundancy coming from derivations of skypatterns among subspaces of the cube [13].…”

Section: Introductionmentioning

confidence: 99%

“…Each subspace stores skypatterns (called proper skypatterns) that do not appear in its descendant ones and the compressed skypattern cube contains only non-empty subspaces. Compared to the original skypattern cube [13], the CSKYC has fewer duplicates among subspaces, and does not need to store all of them. Moreover the cube includes unbalanced skypatterns.…”

Section: Introductionmentioning

confidence: 99%

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Compressing and Querying Skypattern Cubes

Ugarte

Loudni

Boizumault

et al. 2019

Lecture Notes in Computer Science

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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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Section: Definition 2 (Skypattern Operator)mentioning

confidence: 99%

Section: Computing the Cskycmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%