[PDF] Granular Computing And Big Data Advancements eBook

Author : Zhang, Chao
Publisher : IGI Global
Page : 331 pages
File Size : 18,75 MB
Release : 2024-08-06
Category : Computers
ISBN :

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In an era defined by the deluge of data, navigating the complexities of decision-making under conditions of uncertainty has emerged as a formidable challenge for scholars and practitioners alike. The sheer volume and velocity of information inundating decision-makers often leads to paralysis or misguided choices, amplifying the risks inherent in uncertain environments. Granular Computing and Big Data Advancements provides insights and solutions in this challenging landscape. The impact of Granular Computing and Big Data Advancements reverberates across the research community, offering a cohesive resource that bridges the gap between theory and practice. With its interdisciplinary approach and emphasis on innovation, the book fosters collaboration and empowers scholars to tackle complex challenges head-on. Whether researchers seek novel methodologies, practitioners aim to enhance decision-making processes, or students embark on their academic journey, this publication serves as a cornerstone in the quest for effective decision-making amidst the uncertainties of the modern world.

Author : Witold Pedrycz
Publisher : Springer
Page : 444 pages
File Size : 49,2 MB
Release : 2014-07-14
Category : Technology & Engineering
ISBN : 331908254X

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The recent pursuits emerging in the realm of big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry and government organizations. Data sets such as customer transactions for a mega-retailer, weather monitoring, intelligence gathering, quickly outpace the capacities of traditional techniques and tools of data analysis. The 3V (volume, variability and velocity) challenges led to the emergence of new techniques and tools in data visualization, acquisition, and serialization. Soft Computing being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data. This carefully edited volume provides the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of big data architectures, analysis, and interpretation as well as data analytics. The book is aimed at a broad audience of researchers and practitioners including those active in various disciplines in which big data, their analysis and optimization are of genuine relevance. One focal point is the systematic exposure of the concepts, design methodology, and detailed algorithms. In general, the volume adheres to the top-down strategy starting with the concepts and motivation and then proceeding with the detailed design that materializes in specific algorithms and representative applications. The material is self-contained and provides the reader with all necessary prerequisites and augments some parts with a step-by-step explanation of more advanced concepts supported by a significant amount of illustrative numeric material and some application scenarios to motivate the reader and make some abstract concepts more tangible.

Author : Rafael Bello
Publisher : Springer
Page : 339 pages
File Size : 29,41 MB
Release : 2007-12-23
Category : Technology & Engineering
ISBN : 3540769730

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This volume is a compilation of the best papers presented at the First International Symposium on Fuzzy and Rough Sets (ISFUROS 2006) held in Santa Clara, Cuba. They contain valuable contributions both in the theoretical field and in several application domains such as intelligent control, data analysis, decision making and machine learning, just to name a few. Together, they capture the huge potential of the aforementioned methodologies.

Author : Nathalie Japkowicz
Publisher : Springer
Page : 334 pages
File Size : 43,40 MB
Release : 2015-12-16
Category : Technology & Engineering
ISBN : 3319269895

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This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges.

Author : Tsau Young Lin
Publisher : Physica
Page : 538 pages
File Size : 16,45 MB
Release : 2013-11-11
Category : Computers
ISBN : 3790817910

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During the past few years, data mining has grown rapidly in visibility and importance within information processing and decision analysis. This is par ticularly true in the realm of e-commerce, where data mining is moving from a "nice-to-have" to a "must-have" status. In a different though related context, a new computing methodology called granular computing is emerging as a powerful tool for the conception, analysis and design of information/intelligent systems. In essence, data mining deals with summarization of information which is resident in large data sets, while granular computing plays a key role in the summarization process by draw ing together points (objects) which are related through similarity, proximity or functionality. In this perspective, granular computing has a position of centrality in data mining. Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory. Basically, rough set theory may be viewed as a branch of granular computing. However, its applications to data mining have predated that of granular computing.

Author : Seetha, Hari
Publisher : IGI Global
Page : 381 pages
File Size : 13,31 MB
Release : 2017-07-12
Category : Computers
ISBN : 1522528067

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Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.

Author : J. Stepaniuk
Publisher : Springer
Page : 162 pages
File Size : 22,13 MB
Release : 2009-01-29
Category : Computers
ISBN : 3540708014

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This book covers methods based on a combination of granular computing, rough sets, and knowledge discovery in data mining (KDD). The discussion of KDD foundations based on the rough set approach and granular computing feature illustrative applications.

Author : Tanvir Habib Sardar
Publisher : CRC Press
Page : 397 pages
File Size : 47,78 MB
Release : 2024-02-27
Category : Computers
ISBN : 100382272X

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This book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others. Features: Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies. Explains computing models using real-world examples and dataset-based experiments. Includes case studies, quality diagrams, and demonstrations in each chapter. Describes modifications and optimization of existing technologies along with the novel big data computing models. Explores references to machine learning, deep learning, and graph processing. This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.

Author : Haldorai, Anandakumar
Publisher : IGI Global
Page : 263 pages
File Size : 36,25 MB
Release : 2019-09-20
Category : Computers
ISBN : 1522597522

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Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.

Author : H. Fujita
Publisher : IOS Press
Page : 1058 pages
File Size : 18,84 MB
Release : 2018-09-18
Category : Computers
ISBN : 1614999007

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Knowledge-based systems, fully integrated with software, have become essential enablers for both science and commerce. But current software methodologies, tools and techniques are not robust or reliable enough for the demands of a constantly changing and evolving market, and many promising approaches have proved to be no more than case-oriented methods that are not fully automated. This book presents the proceedings of the 17th international conference on New Trends in Intelligent Software Methodology, Tools and Techniques (SoMeT18) held in Granada, Spain, 26-28 September 2018. The SoMeT conferences provide a forum for the exchange of ideas and experience, foster new directions in software development methodologies and related tools and techniques, and focus on exploring innovations, controversies, and the current challenges facing the software engineering community. The 80 selected papers included here are divided into 13 chapters, and cover subjects as diverse as intelligent software systems; medical informatics and bioinformatics; artificial intelligence techniques; social learning software and sentiment analysis; cognitive systems and neural analytics; and security, among other things. Offering a state-of-the-art overview of methodologies, tools and techniques, this book will be of interest to all those whose work involves the development or application of software.