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Pattern Sampling in Distributed Databases

Abstract : Many applications rely on distributed databases. However, only few discovery methods exist to extract patterns without centralizing the data. In fact, this centralization is often less expensive than the communication of extracted patterns from the different nodes. To circumvent this difficulty, this paper revisits the problem of pattern mining in distributed databases by benefiting from pattern sampling. Specifically , we propose the algorithm DDSampling that randomly draws a pattern from a distributed database with a probability proportional to its interest. We demonstrate the soundness of DDSampling and analyze its time complexity. Finally, experiments on benchmark datasets highlight its low communication cost and its robustness. We also illustrate its interest on real-world data from the Semantic Web for detecting outlier entities in DBpedia and Wikidata.
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https://hal.archives-ouvertes.fr/hal-03009021
Contributor : Arnaud Soulet <>
Submitted on : Tuesday, November 17, 2020 - 9:46:41 AM
Last modification on : Monday, November 30, 2020 - 4:26:04 PM
Long-term archiving on: : Thursday, February 18, 2021 - 6:29:26 PM

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Lamine Diop, Cheikh Talibouya Diop, Arnaud Giacometti, Arnaud Soulet. Pattern Sampling in Distributed Databases. Advances in Databases and Information Systems - 24th European Conference, ADBIS 2020, Lyon, France, August 25-27, 2020, Proceedings, pp.60-74, 2020, ⟨10.1007/978-3-030-54832-2_7⟩. ⟨hal-03009021⟩

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