Interval filter: a locality-aware alternative to bloom filters for hardware membership queries by interval classification

Authors:
Ricardo Quislant;Eladio Gutierrez;Oscar Plata;Emilio L. Zapata
Affiliations:
Department of Computer Architecture, University of Málaga, ETSI Informática, Málaga, Spain;Department of Computer Architecture, University of Málaga, ETSI Informática, Málaga, Spain;Department of Computer Architecture, University of Málaga, ETSI Informática, Málaga, Spain;Department of Computer Architecture, University of Málaga, ETSI Informática, Málaga, Spain
Venue:
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Year:
2010

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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.