RT Conference Proceedings
T1 Interval Filter: A Locality-Aware Alternative to Bloom Filters for Hardware Membership Queries by Interval Classification.
A1 Quislant-del-Barrio, Ricardo
A1 Gutiérrez-Carrasco, Eladio Damián
A1 López-Zapata, Emilio
A1 Plata-González, Óscar Guillermo
K1 Compresión de datos (Informática)
K1 Ordenadores - Memorias
AB 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.
PB Springer
YR 2010
FD 2010
LK https://hdl.handle.net/10630/36768
UL https://hdl.handle.net/10630/36768
LA eng
NO Ricardo Quislant, Eladio Gutierrez, Oscar Plata, Emilio L. Zapata. Interval Filter: A Locality-Aware Alternative to Bloom Filters for Hardware Membership Queries by Interval Classification. En Intelligent Data Engineering and Automated Learning (IDEAL'10). Lecture Notes in Computer Science, vol 6283, pp. 162-169, 2010.
NO https://www.springernature.com/la/open-science/policies/book-policies
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026