Duplicated data or redundant data is a main challenge in the field of data science research. Data Deduplication Approaches: Concepts, Strats, and Challenges shows readers the various methods that can be used to eliminate multiple copies of the same files as well as duplicated snts 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 strats and challenges of real- 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 strats 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



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