Data Deduplication Approaches
Concepts, Strategies, and Challenges
- 404 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About This Book
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.
- Includes data deduplication methods for a wide variety of applications
- Includes concepts and implementation strategies that will help the reader to use the suggested methods
- Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable methods for their applications
- Focuses on reduced storage, backup, recovery, and reliability, which are the most important aspects of implementing data deduplication approaches
- Includes case studies
Frequently asked questions
Information
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- About the editors
- Preface
- Acknowledgement
- 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
- Index