This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
Grid Computing and Cluster Computing are advanced topics and latest trends in computer science that find a place in the computer science and information technology curricula of many engineering institutes and universities today. Divided into two parts—Part I, Grid Computing and Part II, Cluster Computing—, this compact and concise text strives to make the concepts of grid computing and cluster computing comprehensible to the students through its fine presentation and accessible style. Part I of the book enables the student not only to understand the concepts involved in grid computing but also to build their own grids for specific applications. Similarly, as today supercomputers are being built using cluster computing architectures, Part II provides an insight into the basic principles involved in cluster computing and equips the readers with the knowledge to build their own clusters in-house. Diagrams are used to illustrate the concepts discussed and to enable the reader to actually construct a grid or a cluster himself. The book is intended as a text for undergraduate and postgraduate students of computer science and engineering, information technology (B.Tech./M.Tech. Computer Science and Engineering/IT), and post-graduate students of computer science/information technology (M.Sc. Computer Science and M.Sc. IT). Besides, practising engineers and computer science professionals should find the text very useful.
The Third Edition of this well-received text analyses the fundamental concepts of data warehousing, data marts, and OLAP. The author discusses, in an easy-to-understand language, important topics such as data mining, how to build a data warehouse, and potential applications of data warehousing technology in government. Besides, the text compares and contrasts the currently available software tools used to design and develop data warehouses. While retaining the six existing case studies, it gives four new case studies: HARBOR, A Highly Available Data Warehouse A Typical Business Data Warehouse for a Trading Company Customer Data Warehouse for the World’s First and Largest Online Bank in the United Kingdom A German Supermarket EDEKA’s Data Warehouse The book, which is a blend of principles and real-life case studies, is intended as a text for students of B.Tech/M.Tech (Computer Science and Engineering), B.Tech/M.Tech (Information Technology), MBA, M.Sc. (Computer Science), M.Sc. (Information Technology), and MCA. It should also be of considerable utility and worth to software professionals and database practitioners.
This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management. This book explores the research issues involved in the application of traditional shallow machine learning and deep learning techniques to big data analytics. It surveys global research advances in extending the conventional unsupervised or clustering algorithms, extending supervised and semi-supervised algorithms and association rule mining algorithms to big data Scenarios. Further it discusses the deep learning applications of big data analytics to fields of computer vision and speech processing, and describes applications such as semantic indexing and data tagging. Lastly it identifies 25 unsolved research problems and research directions in fog computing, as well as in the context of applying deep learning techniques to big data analytics, such as dimensionality reduction in high-dimensional data and improved formulation of data abstractions along with possible directions for their solutions.
This well-received book, now in its third edition, is a comprehensive presentation of the fundamentals of object-oriented database systems (OODBMS). It provides extensive coverage of the different approaches to object data management, including the three major approaches--semantic database systems approach, object-oriented programming language extension approach, and the relational extension approach--as well as the various types of architectures of object-oriented database systems. The book discusses all recent developments in this field, such as the emergence of Java as the dominant object-oriented programming language--resulting in upcoming OODBMS products such as Ozone--and the provision of object-oriented database features in object-relational database systems (ORDBMS) products such as Oracle 9i and DB2. The new edition provides an extensive discussion of PostgreSQL, a popular open source object-oriented database system which has emerged as a viable alternative to expensive commercial database systems such as Oracle. The book is extensively illustrated, which enables students to develop a firm grasp of the underlying concepts. The chapter-end exercises help in testing the students' comprehension of the fundamental principles. The book is primarily meant for students of IT-related programmes having courses in database systems. Computer professionals will also find the book immensely useful.
This comprehensive text, now in its Second Edition, continues to provide the entire spectrum of e-governance—from definition of e-governance to its history, evaluation, e-governance models, infrastructure and manpower facilities, data warehousing possibilities in implementation of e-government projects, and strategies of success of such projects. The text covers 22 case studies—18 Indian case studies and four International case studies. The Indian case studies include Bhoomi, a project of Karnataka Government, CARD (Computer-aided Administration of Registration Department), Smart Nagarpalika (Computerization of Urban Local Bodies or Municipalities), IT in judiciary, Sachivalaya Vahini (e-governance at Secretariat), e-Khazana (Computerization of Treasury Department), and e-Panchayat (Electronic Knowledge-based Panchayat). The international case studies are culled from USA, China, Brazil and Sri Lanka. This book would be of great interest to students of computer science, IT courses, management and public administration. In addition, government departments—both at the centre and in various states—and administrators should find the book highly useful. NEW TO THIS EDITION : Provides two Appendices—one on Eucalyptus cloud to remotely provision e-governance application and another on Revisiting NeGP: eBharath 2020: the proposed future NeGP.
Grid Computing and Cluster Computing are advanced topics and latest trends in computer science that find a place in the computer science and information technology curricula of many engineering institutes and universities today. Divided into two parts—Part I, Grid Computing and Part II, Cluster Computing—, this compact and concise text strives to make the concepts of grid computing and cluster computing comprehensible to the students through its fine presentation and accessible style. Part I of the book enables the student not only to understand the concepts involved in grid computing but also to build their own grids for specific applications. Similarly, as today supercomputers are being built using cluster computing architectures, Part II provides an insight into the basic principles involved in cluster computing and equips the readers with the knowledge to build their own clusters in-house. Diagrams are used to illustrate the concepts discussed and to enable the reader to actually construct a grid or a cluster himself. The book is intended as a text for undergraduate and postgraduate students of computer science and engineering, information technology (B.Tech./M.Tech. Computer Science and Engineering/IT), and post-graduate students of computer science/information technology (M.Sc. Computer Science and M.Sc. IT). Besides, practising engineers and computer science professionals should find the text very useful.
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