Data Deduplication Approaches
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Data Deduplication Approaches
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
Implementing IBM Storage Data Deduplication Solutions
Until now, the only way to capture, store, and effectively retain constantly growing amounts of enterprise data was to add more disk space to the storage infrastructure, an approach that can quickly become cost-prohibitive as information volumes continue to grow and capital budgets for infrastructure do not. In this IBM® Redbooks® publication, we introduce data deduplication, which has emerged as a key technology in dramatically reducing the amount of, and therefore the cost associated with storing, large amounts of data. Deduplication is the art of intelligently reducing storage needs through the elimination of redundant data so that only one instance of a data set is actually stored. Deduplication reduces data an order of magnitude better than common data compression techniques. IBM has the broadest portfolio of deduplication solutions in the industry, giving us the freedom to solve customer issues with the most effective technology. Whether it is source or target, inline or post, hardware or software, disk or tape, IBM has a solution with the technology that best solves the problem. This IBM Redbooks publication covers the current deduplication solutions that IBM has to offer: IBM ProtecTIER® Gateway and Appliance IBM Tivoli® Storage Manager IBM System Storage® N series Deduplication
Research Anthology on Artificial Intelligence Applications in Security
Author: Management Association, Information Resources
language: en
Publisher: IGI Global
Release Date: 2020-11-27
As industries are rapidly being digitalized and information is being more heavily stored and transmitted online, the security of information has become a top priority in securing the use of online networks as a safe and effective platform. With the vast and diverse potential of artificial intelligence (AI) applications, it has become easier than ever to identify cyber vulnerabilities, potential threats, and the identification of solutions to these unique problems. The latest tools and technologies for AI applications have untapped potential that conventional systems and human security systems cannot meet, leading AI to be a frontrunner in the fight against malware, cyber-attacks, and various security issues. However, even with the tremendous progress AI has made within the sphere of security, it’s important to understand the impacts, implications, and critical issues and challenges of AI applications along with the many benefits and emerging trends in this essential field of security-based research. Research Anthology on Artificial Intelligence Applications in Security seeks to address the fundamental advancements and technologies being used in AI applications for the security of digital data and information. The included chapters cover a wide range of topics related to AI in security stemming from the development and design of these applications, the latest tools and technologies, as well as the utilization of AI and what challenges and impacts have been discovered along the way. This resource work is a critical exploration of the latest research on security and an overview of how AI has impacted the field and will continue to advance as an essential tool for security, safety, and privacy online. This book is ideally intended for cyber security analysts, computer engineers, IT specialists, practitioners, stakeholders, researchers, academicians, and students interested in AI applications in the realm of security research.