Multi-metric Graph Query Performance Prediction

  • Keyvan Sasani
  • Mohammad Hossein Namaki
  • Yinghui Wu
  • Assefaw H. Gebremedhin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)


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.



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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Keyvan Sasani
    • 1
  • Mohammad Hossein Namaki
    • 1
  • Yinghui Wu
    • 1
  • Assefaw H. Gebremedhin
    • 1
  1. 1.School of EECSWashington State UniversityPullmanUSA

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