2015/01/08 by Adina Crainiceanu, Daniel Lemire · 2 voices
Computer Science · #cs.DB #cs.DS
paper · pdf · doi:10.1016/j.is.2015.01.002
published as Information Systems Volume 54, December 2015, Pages 311-324
arxiv published 2015/01/08 · crossref created 2015/01/17 · crossref issued 2015/12/01 · crossref published 2015/12/01 · crossref published-print 2015/12/01 · arxiv created 2016/09/21 · arxiv updated 2016/09/22 · crossref deposited 2025/05/17 · crossref indexed 2026/08/02
Bloom filters are probabilistic data structures commonly used for approximate membership problems in many areas of Computer Science (networking, distributed systems, databases, etc.). With the increase in data size and distribution of data, problems arise where a large number of Bloom filters are available, and all them need to be searched for potential matches. As an example, in a federated cloud environment, each cloud provider could encode the information using Bloom filters and share the Bloom filters with a central coordinator. The problem of interest is not only whether a given element is in any of the sets represented by the Bloom filters, but which of the existing sets contain the given element. This problem cannot be solved by just constructing a Bloom filter on the union of all the sets. Instead, we effectively have a multidimensional Bloom filter problem: given an element, we wish to receive a list of candidate sets where the element might be. To solve this problem, we consider 3 alternatives. Firstly, we can naively check many Bloom filters. Secondly, we propose to organize the Bloom filters in a hierarchical index structure akin to a B+ tree, that we call Bloofi. Finally, we propose another data structure that packs the Bloom filters in such a way as to exploit bit-level parallelism, which we call Flat-Bloofi. Our theoretical and experimental results show that Bloofi and Flat-Bloofi provide scalable and efficient solutions alternatives to search through a large number of Bloom filters.