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Optimized and Parallel Query Processing in Similarity-Based Databases

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Modeling Decisions for Artificial Intelligence (MDAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9321))

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Abstract

We present a novel method of query execution in similarity-based databases which adopts techniques commonly used in traditional programming language compilers. Our method is based on decomposition of relational algebra operators into a small set of simple operations which are subject of further optimizations. It shows up that with a small set of optimizations rules our system itself is able to infer efficient algorithms for data processing. Furthermore, operations we propose are compatible with the map/reduce approach to data processing, and thus, allows for implicitly parallel or distributed data processing.

Supported by grant no. 202/12/P167 of the Czech Science Foundation and IGA UP 2014, reg. č. PrF_2014_034.

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Authors and Affiliations

  1. Department of Computer Science, Palacky University, Olomouc 17. Listopadu 12, 77146, Olomouc, Czech Republic

    Petr Krajča

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  1. Petr Krajča
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Correspondence to Petr Krajča .

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Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Vicenc Torra

  2. Toho Gakuen, Tokyo, Japan

    Torra Narukawa

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Krajča, P. (2015). Optimized and Parallel Query Processing in Similarity-Based Databases. In: Torra, V., Narukawa, T. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2015. Lecture Notes in Computer Science(), vol 9321. Springer, Cham. https://doi.org/10.1007/978-3-319-23240-9_14

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  • DOI: https://doi.org/10.1007/978-3-319-23240-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23239-3

  • Online ISBN: 978-3-319-23240-9

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