Bloom Filter
A Data Structure for Computer Networking, Big Data, Cloud Computing, Internet of Things, Bioinformatics and Beyond
- 1st Edition - April 25, 2023
- Imprint: Academic Press
- Authors: Ripon Patgiri, Sabuzima Nayak, Naresh Babu Muppalaneni
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 5 2 0 - 1
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 6 4 6 - 8
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 mo… Read more
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Request a sales quoteBloom 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. Since its inception, the Bloom Filter 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
- Defines concepts and implementation strategies that will help the reader use the suggested methods
- Provides an overview of issues and challenges faced by researchers
Computer scientists, data scientists, and researchers in biomedical engineering and applied informatics students and researchers in data analytics, data science, database architects, and database management researchers and scientists
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Acknowledgments
- Part I: Bloom Filters
- Chapter 1: Introduction
- Abstract
- 1.1. Introduction
- 1.2. Organization
- References
- Chapter 2: Bloom Filters: a powerful membership data structure
- Abstract
- 2.1. Introduction
- 2.2. Bloom Filter
- 2.3. Bit array
- 2.4. Taxonomy of response
- 2.5. Key objectives
- 2.6. Taxonomy of Bloom Filter
- 2.7. Analysis of false positive
- 2.8. Lessons learned
- 2.9. Conclusion
- Appendix 2.A. Source code of Bloom Filter
- Appendix 2.B. Symbols used in the chapter
- References
- Chapter 3: robustBF: a high accuracy and memory efficient 2D Bloom Filter for diverse applications
- Abstract
- 3.1. Introduction
- 3.2. Preliminaries
- 3.3. robustBF – the proposed system
- 3.4. Experimental results
- 3.5. Analysis
- 3.6. Conclusion
- References
- Chapter 4: Impact of the hash functions in Bloom Filter design
- Abstract
- 4.1. Introduction
- 4.2. Hash functions
- 4.3. Prime numbers in Bloom Filter
- 4.4. Number of hash functions
- 4.5. Types of hash functions
- 4.6. Comparison of robustBF and libbloom
- 4.7. Conclusion
- References
- Chapter 5: Analysis on Bloom Filter: performance, memory, and false positive probability
- Abstract
- 5.1. Introduction
- 5.2. False positive probability
- 5.3. Memory
- 5.4. Performance
- 5.5. Conclusion
- References
- Chapter 6: Does not Bloom Filter bloom in membership filtering?
- Abstract
- 6.1. Introduction
- 6.2. Bloom Filter is not a complete system!
- 6.3. Learned Bloom Filter
- 6.4. Malicious URL filtering using Bloom Filter
- 6.5. Conclusion
- References
- Chapter 7: Standard of Bloom Filter: a review
- Abstract
- 7.1. Introduction
- 7.2. Literature review on standard Bloom Filter
- 7.3. Other approximation filters
- 7.4. Conclusion
- References
- Chapter 8: Counting Bloom Filter: architecture and applications
- Abstract
- 8.1. Introduction
- 8.2. Counting Bloom Filter
- 8.3. Variants
- 8.4. Issues
- 8.5. Conclusion
- References
- Chapter 9: Hierarchical Bloom Filter
- Abstract
- 9.1. Introduction
- 9.2. Variants
- 9.3. Issues
- 9.4. Applications
- 9.5. Conclusion
- References
- Part II: Applications of Bloom Filter in networking
- Chapter 10: Applications of Bloom Filter in networking and communication
- Abstract
- 10.1. Introduction
- 10.2. Traffic management
- 10.3. Packet management
- 10.4. Routing
- 10.5. Searching
- 10.6. Discussion
- 10.7. Conclusion
- References
- Chapter 11: Bloom Filter for named-data networking
- Abstract
- 11.1. Introduction
- 11.2. Named data networking
- 11.3. Named data networking packet
- 11.4. Content store
- 11.5. Pending interest table
- 11.6. Forwarding information base
- 11.7. Security
- 11.8. Discussion
- 11.9. Conclusion
- References
- Chapter 12: Enhancement of software-defined networking using Bloom Filter
- Abstract
- 12.1. Introduction
- 12.2. SDN architecture
- 12.3. Control layer
- 12.4. Data layer
- 12.5. Issues and challenges
- 12.6. Security
- 12.7. Discussion
- 12.8. Conclusion
- References
- Chapter 13: Impact of Bloom Filter in wireless network
- Abstract
- 13.1. Introduction
- 13.2. Wireless sensor networks
- 13.3. Mobile ad-hoc networks
- 13.4. Internet-of-Things
- 13.5. Discussion
- 13.6. Conclusion
- References
- Chapter 14: Network security using Bloom Filter
- Abstract
- 14.1. Introduction
- 14.2. DDoS
- 14.3. DoS
- 14.4. Defense against various network attacks
- 14.5. Security
- 14.6. Privacy
- 14.7. Evaluation
- 14.8. Discussion
- 14.9. Conclusion
- References
- Part III: Applications of Bloom Filter in other domains
- Chapter 15: Applications of Bloom Filter in Big data
- Abstract
- 15.1. Introduction
- 15.2. Data management
- 15.3. Database
- 15.4. MapReduce
- 15.5. Discussion
- 15.6. Conclusion
- References
- Chapter 16: Bloom Filter in cloud computing
- Abstract
- 16.1. Introduction
- 16.2. Indexing and searching of encrypted data
- 16.3. Cloud data storage management
- 16.4. Discussion
- 16.5. Conclusion
- References
- Chapter 17: Applications of Bloom Filter in biometrics
- Abstract
- 17.1. Introduction
- 17.2. Biometrics
- 17.3. Cancelable biometrics
- 17.4. Discussion
- 17.5. Conclusion
- References
- Chapter 18: Bloom Filter for bioinformatics
- Abstract
- 18.1. Introduction
- 18.2. Preprocessing filtering
- 18.3. de Bruijn graph
- 18.4. Searching
- 18.5. DNA assembly technique
- 18.6. Other bioinformatics areas
- 18.7. Discussion
- 18.8. Conclusion
- References
- Index
- Edition: 1
- Published: April 25, 2023
- No. of pages (Paperback): 228
- No. of pages (eBook): 228
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128235201
- eBook ISBN: 9780128236468
RP
Ripon Patgiri
Dr. Ripon Patgiri is an Assistant Professor at the Department of Computer Science & Engineering, National Institute of Technology Silchar, since 2013. His research interests include bloom filters, storage systems, security, and cryptography computing. He has published numerous papers in reputed journals, conferences, and books. Also, he has been awarded with several international patents. He is a senior member of IEEE. He was the General Chair of ICACNI 2018 and BigDML 2019. He is the Organizing Chair of FRSM 2020 and ADCOM 2020. Also, he is the Program Chair of CoMSO 2020, CoMSO 2021, and CoMSO 2022. He is also an editor of several multi-authored books. Moreover, he has received two research project fundings from SERB and DST, India.
Affiliations and expertise
Assistant Professor, Department of Computer Science and Engineering, National Institute of Technology, Silchar, IndiaSN
Sabuzima Nayak
Sabuzima Nayak has published numerous papers in reputed journals, conferences, and books. Her research interests include bioinformatics, Bloom Filter, Big Data, and distributed systems.
Affiliations and expertise
Research Scholar, Department of Computer Science and Engineering, National Institute of Technology, Silchar, IndiaNB
Naresh Babu Muppalaneni
Dr. Naresh Babu Muppalaneni is the author of several books in the field of Computational Intelligence and bioinformatics, including Computational Intelligence Techniques in Diagnosis of Brain Diseases, Soft Computing and Medical Bioinformatics, Computational Intelligence in Medical Informatics, and Computational Intelligence Techniques for Comparative Genomics, all from Springer, as well as Computational Study on Protein-Ligand Interactions for Anti-Diabetic: In Silico Study from Lambert Academic Publishing He is a Senior Member of IEEE, and his research interests include Machine Learning, Computational Systems Biology, bioinformatics, Artificial Intelligence in Biomedical Engineering, applications of intelligent system techniques, image processing, and social network analysis.
Affiliations and expertise
Assistant Professor, Department of Computer Science and Engineering, National Institute of Technology, Silchar, IndiaRead Bloom Filter on ScienceDirect