Data Deduplication Approaches
Concepts, Strategies, and Challenges

Examines the concepts and technical methods of data deduplication for a wide variety of applications

Tin Thein Thwel (Edited by), G. R. Sinha (Edited by)

9780128233955

Paperback, published 27 November 2020

404 pages, 230 illustrations (30 in full color)
23.4 x 19 x 2.6 cm, 0.81 kg

In the age of data science, the rapidly increasing amount of data is a major concern in numerous applications of computing operations and data storage. Duplicated data or redundant data is a main challenge in the field of data science research. Data Deduplication Approaches: Concepts, Strategies, and Challenges shows readers the various methods that can be used to eliminate multiple copies of the same files as well as duplicated segments or chunks of data within the associated files. Due to ever-increasing data duplication, its deduplication has become an especially useful field of research for storage environments, in particular persistent data storage. Data Deduplication Approaches provides readers with an overview of the concepts and background of data deduplication approaches, then proceeds to demonstrate in technical detail the strategies and challenges of real-time implementations of handling big data, data science, data backup, and recovery. The book also includes future research directions, case studies, and real-world applications of data deduplication, focusing on reduced storage, backup, recovery, and reliability.

1. Introduction to data deduplication approaches
2. Data deduplication concepts
3. Concepts, strategies, and challenges of data deduplication
4. Existing mechanisms for data deduplication
5. Classification criteria for data deduplication methods
6. File chunking approaches
7. Study of data deduplication for file chunking approaches
8. Essentials of data deduplication using open-source toolkit
9. Efficient data deduplication scheme for scale-out distributed storage
10. Identification of duplicate bug reports in software bug repositories: a systematic review, challenges and future scope
11. A survey and critical analysis on energy generation from datacenter
12. Review of MODIS EVI and NDVI data for data mining applications
13. Performance modeling for secure migration processes of legacy systems to the cloud computing
14. DedupCloud: an optimized efficient virtual machine deduplication algorithm in cloud computing environment
15. Data deduplication for cloud storage
16. Data duplication using Amazon Web Services cloud storage
17. Game-theoretic analysis of encrypted cloud data deduplication
18. Data deduplication applications in cognitive science and computer vision research

Subject Areas: Biomedical engineering [MQW]