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Efficiently Mining Large Gradual Patterns Using Chunked Storage Layout

Dickson Owuor 1 Anne Laurent 2
2 FADO - Fuzziness, Alignments, Data & Ontologies
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Existing approaches for extracting gradual patterns become inefficient in terms of memory usage when applied on data sets with huge numbers of objects. This inefficiency is caused by the contiguous nature of loading binary matrices into main memory as single blocks when validating candidate gradual patterns. This paper proposes an efficient storage layout that allows these matrices to be split and loaded into/from memory in multiple smaller chunks. We show how HDF5 (Hierarchical Data Format version 5) may be used to implement this chunked layout and our experiments reveal a great improvement in memory usage efficiency especially on huge data sets.
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Contributor : Anne Laurent <>
Submitted on : Monday, August 16, 2021 - 5:25:34 PM
Last modification on : Saturday, August 28, 2021 - 3:19:36 AM


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Dickson Owuor, Anne Laurent. Efficiently Mining Large Gradual Patterns Using Chunked Storage Layout. 25th European Conference on Advances in Databases and Information Systems (ADBIS 2021), Aug 2021, Tartu, Estonia. pp.30-42, ⟨10.1007/978-3-030-82472-3_4⟩. ⟨lirmm-03320961⟩



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