Sketch-Based Multi-query Processing over Data Streams

  • Alin Dobra
  • Minos Garofalakis
  • Johannes Gehrke
  • Rajeev Rastogi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2992)


Recent years have witnessed an increasing interest in designing algorithms for querying and analyzing streaming data (i.e., data that is seen only once in a fixed order) with only limited memory. Providing (perhaps approximate) answers to queries over such continuous data streams is a crucial requirement for many application environments; examples include large telecom and IP network installations where performance data from different parts of the network needs to be continuously collected and analyzed.

Randomized techniques, based on computing small “sketch” synopses for each stream, have recently been shown to be a very effective tool for approximating the result of a single SQL query over streaming data tuples. In this paper, we investigate the problems arising when data-stream sketches are used to process multiple such queries concurrently. We demonstrate that, in the presence of multiple query expressions, intelligently sharing sketches among concurrent query evaluations can result in substantial improvements in the utilization of the available sketching space and the quality of the resulting approximation error guarantees. We provide necessary and sufficient conditions for multi-query sketch sharing that guarantee the correctness of the result-estimation process. We also prove that optimal sketch sharing typically gives rise to \(\mathcal{NP}\)-hard questions, and we propose novel heuristic algorithms for finding good sketch-sharing configurations in practice. Results from our experimental study with realistic workloads verify the effectiveness of our approach, clearly demonstrating the benefits of our sketch-sharing methodology.


Data Stream Query Answer Space Allocation Query Processor Aggregate Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Alin Dobra
    • 1
  • Minos Garofalakis
    • 2
  • Johannes Gehrke
    • 3
  • Rajeev Rastogi
    • 2
  1. 1.University of FloridaGainesvilleUSA
  2. 2.Bell LaboratoriesLucent TechnologiesMurray HillUSA
  3. 3.Cornell UniversityIthacaUSA

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