Bloom Filter: A Data Structure for Computer Networking, Big Data, Cloud Computing, Internet of Things, Bioinformatics, and Beyond focuses on both the theory and practice of the most emerging areas for Bloom filter application, including Big Data, Cloud Computing, Internet of Things, and Bioinformatics. Sections provide in-depth insights on structure and variants, focus on its role in computer networking, and discuss applications in various research domains, such as Big Data, Cloud Computing, and Bioinformatics. The conventional Bloom filter is a probabilistic data structure for membership filter. Burton Howard Bloom introduced an approximate membership filtering data structure in 1970. Hence, it is called a Bloom filter. Since its inception, it has been extensively experimented with and developed to enhance system performance such as web cache. Bloom filter influences many research fields, including Bioinformatics, Internet of Things, computer security, network appliances, Big Data, and Cloud Computing. Includes Bloom filter methods for a wide variety of applications Includes concepts and implementation strategies that will help the reader to use the suggested methods Provides a look at issues and challenges faced by researchers
- | Author: Ripon Patgiri, Sabuzima Nayak, Naresh Babu Muppalaneni
- | Publisher: Academic Press
- | Publication Date: Apr 28, 2023
- | Number of Pages: 228 pages
- | Language: English
- | Binding: Paperback
- | ISBN-10: 0128235209
- | ISBN-13: 9780128235201
