Bloom Filter

A Data Structure for Computer Networking, Big Data, Cloud Computing, Internet of Things, Bioinformatics and Beyond

1st Edition - April 25, 2023
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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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. 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.

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

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, India

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, India

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, India

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Bloom Filter

A Data Structure for Computer Networking, Big Data, Cloud Computing, Internet of Things, Bioinformatics and Beyond

Purchase options

Institutional subscription on ScienceDirect

Ripon Patgiri

Sabuzima Nayak

Naresh Babu Muppalaneni