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Elsevier

Bloom Filter

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

  • 1st Edition - April 25, 2023
  • Latest edition
  • Authors: Ripon Patgiri, Sabuzima Nayak, Naresh Babu Muppalaneni
  • Language: English

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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Description

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.

Key features

  • 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

Readership

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

Table of contents

Section 1 Bloom Filters

1. Introduction to Bloom Filter

2. Bloom Filter: A Powerful Membership Data Structure

3. robustBF: A High Accuracy and Memory Efficient 2D Bloom Filter

4. Impact of the Hash Functions in Bloom Filters

5. Analysis on Bloom Filter: Performance, Memory and False Positive Probability

6. Does not Bloom Filter bloom in Membership Filtering?

7. A review on Standard Bloom Filter

8. Counting Bloom Filter: Architecture and Applications

9. Hierarchical Bloom Filter

Section 2 Applications of Bloom Filter in Networking

10. Application of Bloom Filter in Networking and Communication

11. Content-Centric Network

12. Software-Defined Network

13. Wireless Networking

14. Network Security

Section 3 Applications of Bloom Filter in Other Domains

15. Big Data

16. Cloud Computing

17. Biometrics

18. Bioinformatics

Product details

  • Edition: 1
  • Latest edition
  • Published: April 28, 2023
  • Language: English

About the authors

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, India

SN

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

NB

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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