2006/11/28 by Paulo Sérgio Almeida, Carlos Baquero, Nuno Preguiça +1 · 3 citations
Computer Science · Materials Science · Mathematics · #Caching and Content Delivery #Opportunistic and Delay-Tolerant Networks #Covalent Organic Framework Applications #Bloom filter #Scalability #Set (abstract data type) #False positive paradox #Computer science #Filter (signal processing) #A priori and a posteriori #Algorithm #Mathematics #Artificial intelligence #Database
paper · doi:10.1016/j.ipl.2006.10.007
openalex publication_date 2006/11/28 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/23
Bloom filters provide space-efficient storage of sets at the cost of a probability of false positives on membership queries. The size of the filter must be defined a priori based on the number of elements to store and the desired false positive probability, being impossible to store extra elements without increasing the false positive probability. This leads typically to a conservative assumption regarding maximum set size, possibly by orders of magnitude, and a consequent space waste. This paper proposes Scalable Bloom Filters, a variant of Bloom filters that can adapt dynamically to the number of elements stored, while assuring a maximum false positive probability.