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Multi-metric Graph Query Performance Prediction

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Database Systems for Advanced Applications (DASFAA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10827))

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Abstract

We propose a general framework for predicting graph query performance with respect to three performance metrics: execution time, query answer quality, and memory consumption. The learning framework generates and makes use of informative statistics from data and query structure and employs a multi-label regression model to predict the multi-metric query performance. We apply the framework to study two common graph query classes—reachability and graph pattern matching; the two classes differ significantly in their query complexity. For both query classes, we develop suitable performance models and learning algorithms to predict the performance. We demonstrate the efficacy of our framework via experiments on real-world information and social networks. Furthermore, by leveraging the framework, we propose a novel workload optimization algorithm and show that it improves the efficiency of workload management by 54% on average.

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Notes

  1. 1.

    https://snap.stanford.edu/data/soc-pokec.html.

  2. 2.

    http://wiki.dbpedia.org/.

  3. 3.

    http://scikit-learn.org.

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Acknowledgments

Sasani and Gebremedhin are supported in part by NSF CAREER award IIS-1553528. Namaki and Wu are supported in part by NSF IIS-1633629 and Huawei Innovation Research Program (HIRP).

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Authors and Affiliations

  1. School of EECS, Washington State University, Pullman, USA

    Keyvan Sasani, Mohammad Hossein Namaki, Yinghui Wu & Assefaw H. Gebremedhin

Authors
  1. Keyvan Sasani
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  2. Mohammad Hossein Namaki
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  3. Yinghui Wu
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  4. Assefaw H. Gebremedhin
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Corresponding author

Correspondence to Keyvan Sasani .

Editor information

Editors and Affiliations

  1. Simon Fraser University, Burnaby, BC, Canada

    Jian Pei

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

  3. University of Queensland, Brisbane, QLD, Australia

    Shazia Sadiq

  4. University of Western Australia, Crawley, WA, Australia

    Jianxin Li

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Sasani, K., Namaki, M.H., Wu, Y., Gebremedhin, A.H. (2018). Multi-metric Graph Query Performance Prediction. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_19

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  • DOI: https://doi.org/10.1007/978-3-319-91452-7_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91451-0

  • Online ISBN: 978-3-319-91452-7

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