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Conference Papers Year : 2019

Extracting Fuzzy Gradual Patterns from Property Graphs

Abstract

A property graph in a NoSQL graph database engine provides an efficient way to manage the data and knowledge due to its native graph-structure storage. A property graph is a labeled directed graph having nodes and relationships with a set of attributes or properties in form of (key:value) pairs. In this work, we aim at mining such graphs in order to extract frequent gradual patterns in the form of "the more/less A1,..., the more/less An" where Ai are information from the graph, should it be from the nodes or from the relationships. In order to retrieve more valuable patterns, we consider fuzzy gradual patterns in the form of "The more/less the A1 is F1,...,the more/less the An is Fn" where Ai are attributes retrieved from the graph nodes or relationships and Fi are fuzzy descriptions. For this purpose, we introduce the definitions of such concepts, the corresponding method for extracting the patterns, and the experiments that we have led on synthetic graphs using a graph generator. We show the results in terms of time and memory consumption.
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Dates and versions

lirmm-02085780 , version 1 (07-10-2019)

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Faaiz Hussain Shah, Arnaud Castelltort, Anne Laurent. Extracting Fuzzy Gradual Patterns from Property Graphs. FUZZ-IEEE 2019 - International Conference on Fuzzy Systems, Jun 2019, New-Orleans, United States. pp.1-6, ⟨10.1109/FUZZ-IEEE.2019.8858936⟩. ⟨lirmm-02085780⟩
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