Workload-Optimal Histograms on Streams

  • S. Muthukrishnan
  • M. Strauss
  • X. Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3669)


A histogram is a piecewise-constant approximation of an observed data distribution. A histogram is used as a small-space, approximate synopsis of the underlying data distribution, which is often too large to be stored precisely. Histograms have found many applications in database management systems, perhaps most commonly for query selectivity estimation in query optimizers [1], but have also found applications in approximate query answering [2], load balancing in parallel join execution [3], mining time-series data [4], partition-based temporal join execution, query pro.ling for user feedback, etc. Ioannidis has a nice overview of the history of histograms, their applications, and their use in commercial DBMSs [5]. Also, Poosala’s thesis provides a systematic treatment of different types of histograms [3].


Point Query Weight Class Lossy Compression Time Poly Wavelet Synopsis 
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 2005

Authors and Affiliations

  • S. Muthukrishnan
    • 1
  • M. Strauss
    • 2
  • X. Zheng
    • 2
  1. 1.Supported by NSF ITR 0220280 and NSF 0354600, Rutgers University 
  2. 2.Supported by NSF DMS 0354600, University of Michigan 

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