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Interval Filter: A Locality-Aware Alternative to Bloom Filters for Hardware Membership Queries by Interval Classification

  • Ricardo Quislant
  • Eladio Gutierrez
  • Oscar Plata
  • Emilio L. Zapata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6283)

Abstract

Bloom filters are data structures that can efficiently represent a set of elements providing operations of insertion and membership testing. Nevertheless, these filters may yield false positive results when testing for elements that have not been previously inserted. In general, higher false positive rates are expected for sets with larger cardinality with constant filter size. This paper shows that for sets where a distance metric can be defined, reducing the false positive rate is possible if elements to be inserted exhibit locality according to this metric. In this way, a hardware alternative to Bloom filters able to extract spatial locality features is proposed and analyzed.

Keywords

Hash Function Bloom Filter Transactional Memory Cache Block Valid Interval 
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 2010

Authors and Affiliations

  • Ricardo Quislant
    • 1
  • Eladio Gutierrez
    • 1
  • Oscar Plata
    • 1
  • Emilio L. Zapata
    • 1
  1. 1.Department of Computer ArchitectureUniversity of Málaga, ETSI Informática, Campus TeatinosMálagaSpain

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