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Flower Master Index for Relational Database Selection and Joining

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Towards Digital Intelligence Society (DISA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1281))

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Abstract

Current relational database systems are forming the main part of the intelligent information and support system. Data inside are secured, protected by the relational integrity covered by the transactions. Access and management are done by the relational algebra. Significant aspects expressing the quality and performance are the data access techniques themselves. The index is one of the key features ensuring data retrieval performance, however, from the other operations point of view, it brings additional costs. If the suitable index for the query is not present, the whole table must be scanned sequentially resulting in poor performance. If the data fragmentation is present, the problem is even deeper. This paper aims to propose its own techniques based on relevant block identification objects stored in private or shared memory areas. It uses rebalancing methods on the extent granularity. If the tables are to be joined and both indexes are not present, usually, the Hash join method is used. The technique of two-dimensional index mapper is discussed in the paper, as well.

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Acknowledgment

This publication was realized with support of the Operational Programme Integrated Infrastructure in frame of the project: Intelligent systems for UAV real-time operation and data processing, code ITMS2014+: 313011V422 and co-financed by the European Regional Development Found.

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Authors and Affiliations

  1. Žilinská univerzita v Žiline Fakulta riadenia a informatiky, 01026, Žilina, Slovakia

    Michal Kvet & Karol Matiaško

Authors
  1. Michal Kvet
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  2. Karol Matiaško
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Corresponding author

Correspondence to Michal Kvet .

Editor information

Editors and Affiliations

  1. Department of Cybernetics and AI, Technical University of Košice, Košice, Slovakia

    Ján Paralič

  2. Department of Cybernetics and AI, Technical University of Košice, Košice, Slovakia

    Peter Sinčák

  3. Department of Electrical and Electronics Engineering, Chukyo University, Nagoya, Japan

    Pitoyo Hartono

  4. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague 6, Czech Republic

    Vladimír Mařík

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Kvet, M., Matiaško, K. (2021). Flower Master Index for Relational Database Selection and Joining. In: Paralič, J., Sinčák, P., Hartono, P., Mařík, V. (eds) Towards Digital Intelligence Society. DISA 2020. Advances in Intelligent Systems and Computing, vol 1281. Springer, Cham. https://doi.org/10.1007/978-3-030-63872-6_9

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