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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2016: Intelligent Information and Database Systems pp 691–700Cite as

Optimisation of Query Processing with Multilevel Storage

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9622))

Abstract

The typical algorithms for optimization of query processing in database systems do not take under the consideration the availability of different types and sizes of persistent and transient storage resource that can be used to speed up the internal query processing. It is well known that appropriate allocation of storage resources for the internal query processing may significantly improve performance. This paper describes the new algorithms for automatic management of multilevel transient and persistent storage resources in order to optimize the performance of query processing in a database system. The algorithms presented in the paper process the concurrently submitted queries and discover the common query processing plans. The algorithms estimate the query processing costs and choose the best allocation of multilevel storage resources to optimise the overall internal query processing costs. The paper presents the outcomes of experiments that confirm the improvements in performance through appropriate allocation of multilevel storage for the internal query processing.

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References

  1. HGST: tiered optimization (2015). http://global.hgst.com/science-of-storage/technology-insights/tiered-storage-optimization-data-center-performance-and-tco. Accessed 25 August 2015

  2. Rodd, S., Kulkarni, U.: Adaptive self-tuning techniques for performance tuning of database systems: a fuzzy-based approach. In: 2013 2nd International Conference on Advanced Computing, Networking and Security (ADCONS), pp. 124–129. IEEE (2013)

    Google Scholar 

  3. Schnaitter, K.: On-line Index Selection for Physical Database Tuning. Ph.D. thesis, Santa Cruz (2010)

    Google Scholar 

  4. Surajit, C., Vivek, N.: Self-tuning database systems: a decade of progress. In: Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 3–14. VLDB Endowment Inc. (2007)

    Google Scholar 

  5. Trancoso, P., Torrellas, J.: Cache optimization for memory-resident decision support commercial workloads. In: International Conference on Computer Design (ICCD 1999), pp. 546–554 (1999)

    Google Scholar 

  6. Murphy, M., Shan, M.C.: Execution plan balancing. In: Proceedings of the Seventh International Conference on Data Engineering, pp. 698–706 (1991)

    Google Scholar 

  7. TPC:TPC (2015). http://www.tpc.org/information/benchmarks.asp. Accessed 25 March 2015

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Author information

Authors and Affiliations

  1. School of Computer Science and Software Engineering, University of Wollongong, Wollongong, Australia

    Nan N. Noon & Janusz R. Getta

Authors
  1. Nan N. Noon
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  2. Janusz R. Getta
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Corresponding author

Correspondence to Nan N. Noon .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

  3. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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© 2016 Springer-Verlag Berlin Heidelberg

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Cite this paper

Noon, N.N., Getta, J.R. (2016). Optimisation of Query Processing with Multilevel Storage. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_67

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  • DOI: https://doi.org/10.1007/978-3-662-49390-8_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49389-2

  • Online ISBN: 978-3-662-49390-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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