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ICDE 2012 Accepted Paper

December 12th, 2011

The Brown Data Management Research Group has the following paper in ICDE 2012:

  • Learning-based Query Performance Modeling and Prediction
       Mert Akdere, Ugur Cetintemel, Matteo Riondato, Eli Upfal, Stanley Zdonik (Project Longview)

    This paper studies the practicality and utility of sophisticated learning-based models for accurate query performance prediction (QPP). We propose and evaluate predictive modeling techniques that learn query execution behavior at different granularities, ranging from coarse-grained plan-level models to fine-grained operator-level models. We demonstrate that these two extremes offer a tradeoff between high accuracy for static workload queries and generality to unforeseen queries in dynamic workloads, respectively, and introduce a hybrid approach that combines their respective strengths by selectively composing them in the process of QPP. We discuss how we can use a training workload to (i) pre-build and materialize such models offline, so that they are readily available for future predictions, and (ii) build new models online as new predictions are needed. All prediction models are built using only static features (available prior to query execution) and the performance values obtained from the offline execution of the training workload. The results of an extensive experimental evaluation provide quantitative evidence that learning-based modeling for QPP is both feasible and effective for both static and dynamic workload scenarios.