Distance Sensitive Bloom Filters Without False Negatives

Mayank Goswami, Rasmus Pagh, Francesco Silvestri, Johan Sivertsen

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review


A Bloom filter is a widely used data-structure for representing a set S and answering queries of the form “Is x in S?”. By allowing some false positive answers (saying ‘yes’ when the answer is in fact ‘no’) Bloom filters use space significantly below what is required for storing S. In the distance sensitive setting we work with a set S of (Hamming) vectors and seek a data structure that offers a similar trade-off, but answers queries of the form “Is x close to an element of S?” (in Hamming distance). Previous work on distance sensitive Bloom filters have accepted false positive and false negative answers. Absence of false negatives is of critical importance in many applications of Bloom filters, so it is natural to ask if this can be also achieved in the distance sensitive setting. Our main contributions are upper and lower bounds (that are tight in several cases) for space usage in the distance sensitive setting where false negatives are not allowed.
TitelProceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017, Barcelona, Spain, Hotel Porta Fira, January 16-19
Antal sider13
ForlagSociety for Industrial and Applied Mathematics
ISBN (Elektronisk)978-1-61197-478-2
StatusUdgivet - 2017


Dyk ned i forskningsemnerne om 'Distance Sensitive Bloom Filters Without False Negatives'. Sammen danner de et unikt fingeraftryk.