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Bloom Filters for Efficient Coupling Between Tables of a Database

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Engineering Applications of Neural Networks (EANN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 744))

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Abstract

Nowadays, digital data are the most valuable asset of almost every organization. Database management systems are considered as storing systems for efficient retrieval and processing of digital data. However, effective operation, in terms of data access speed and relational database is limited, as its size increases significantly [6]. Bloom filter is a special data structure with finite storage requirements and rapid control of an object membership to a dataset. It is worth mentioning that the Bloom filter structure has been proposed with a view to constructively increase data access in relational databases. Since the characteristics of a Bloom filter are consistent with the requirements of a fast data access structure, we examine the possibility of using it in order to increase the SQL query execution speed in a database. In the context of this research, a database in a RDBMS SQL Server that includes big data tables is implemented and in following the performance enhancement, using Bloom filters, in terms of execution time on different categories of SQL queries, is examined. We experimentally proved the time effectiveness of Bloom filter structure in relational databases when dealing with large scale data.

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Notes

  1. 1.

    http://odetocode.com/articles/237.aspx.

  2. 2.

    http://dataidol.com/tonyrogerson/2013/05/09/reducing-sql-server-io-and-access-times-using-Bloom-filters-part-2-basics-of-the-method-in-sql-server.

  3. 3.

    https://www.perl.com/pub/2004/04/08/bloom_filters.html.

  4. 4.

    https://blog.medium.com/what-are-bloom-filters-1ec2a50c68ff.

  5. 5.

    https://llimllib.github.io/bloomfilter-tutorial.

  6. 6.

    https://prakhar.me/articles/bloom-filters-for-dummies.

  7. 7.

    http://bugra.github.io/work/notes/2016-06-05/a-gentle-introduction-to-bloom-filter.

  8. 8.

    http://db-engines.com/en/ranking/relational+dbms.

  9. 9.

    http://www.w3schools.com/sql/sql_syntax.asp.

  10. 10.

    https://www.techonthenet.com/sql/in.php.

  11. 11.

    https://www.techonthenet.com/sql/exists.php.

  12. 12.

    https://docs.microsoft.com/en-us/sql/t-sql/queries/top-transact-sql.

  13. 13.

    https://www.simple-talk.com/sql/learn-sql-server/sql-server-index-basics.

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Author information

Authors and Affiliations

  1. Computer Engineering and Informatics Department, University of Patras, Patras, Greece

    Eirini Chioti, Elias Dritsas, Andreas Kanavos & Athanasios Tsakalidis

  2. Department of Informatics, Ionian University, Corfu, Greece

    Spyros Sioutas

  3. Interamerican, Athina, Greece

    Xenophon Liapakis

Authors
  1. Eirini Chioti
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  2. Elias Dritsas
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  3. Andreas Kanavos
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  4. Xenophon Liapakis
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  5. Spyros Sioutas
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  6. Athanasios Tsakalidis
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Corresponding author

Correspondence to Andreas Kanavos .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

  2. Democritus University of Thrace, University Campus, Xanthi, Greece

    Lazaros Iliadis

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Univesity of Ioannina, Ioannina, Greece

    Aristidis Likas

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Chioti, E., Dritsas, E., Kanavos, A., Liapakis, X., Sioutas, S., Tsakalidis, A. (2017). Bloom Filters for Efficient Coupling Between Tables of a Database. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_50

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  • DOI: https://doi.org/10.1007/978-3-319-65172-9_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65171-2

  • Online ISBN: 978-3-319-65172-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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