TL;DR
This paper introduces Bloofi, a hierarchical index structure, and Flat-Bloofi, a bit-parallel packing method, to efficiently search through large collections of Bloom filters in distributed systems.
Contribution
The paper presents two novel data structures, Bloofi and Flat-Bloofi, designed to improve scalability and efficiency in multidimensional Bloom filter searches.
Findings
Bloofi outperforms naive search methods in large-scale scenarios.
Flat-Bloofi exploits bit-level parallelism for faster searches.
Both structures are scalable and efficient for distributed systems.
Abstract
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…
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