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International Conference on Data Mining and Big Data

DMBD 2016: Data Mining and Big Data pp 77–85Cite as

Oracle and Vertica for Frequent Itemset Mining

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9714))

Abstract

In the last few years, organizations have become much more interested in using data to create value. Big Data, however, presents new challenges to the extraction of knowledge using traditional Data Mining methods. In this paper we focus on a concrete implementation of association rules generation. The proposed algorithm is specialized for four datasets and its performance for different support thresholds is measured. This is done for two Database Management Systems (DBMS) – a traditional row-oriented DMBS in the face of Oracle and a column-oriented DBMS represented by Vertica. The results indicate the suitability of these DBMSs as tools for association rules generation.

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Acknowledgments

This work is partially supported by Sofia University SRF/2016 under contract “Big data: analysis and management of data and projects” and FMI – Sofia University.

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Authors and Affiliations

  1. Faculty of Mathematics and Informatics, Sofia University, 5 James Bourchier Blvd, 1164, Sofia, Bulgaria

    Hristo Kyurkchiev & Kalinka Kaloyanova

Authors
  1. Hristo Kyurkchiev
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  2. Kalinka Kaloyanova
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Corresponding author

Correspondence to Kalinka Kaloyanova .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

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© 2016 Springer International Publishing Switzerland

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Kyurkchiev, H., Kaloyanova, K. (2016). Oracle and Vertica for Frequent Itemset Mining. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-40973-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40972-6

  • Online ISBN: 978-3-319-40973-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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