Encyclopedia of Big Data Technologies, 1st ed. 2019

Langue : Anglais

Couverture de l’ouvrage Encyclopedia of Big Data Technologies

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The Encyclopedia of Big Data Technologies provides researchers, educators, students and industry professionals with a comprehensive authority over the most relevant Big Data Technology concepts. With over 300 articles written by worldwide subject matter experts from both industry and academia, the encyclopedia covers topics such as big data storage systems, NoSQL database, cloud computing, distributed systems, data processing, data management, machine learning and social technologies, data science. Each peer-reviewed, highly structured entry provides the reader with basic terminology, subject overviews, key research results, application examples, future directions, cross references and a bibliography. The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals. In addition, the distinguished, international editorial board of the encyclopedia consists of well-respected scholars, each developing topics based upon their expertise.

Big Data Integration; Big SQL; Big Spatial Data Management; Big Semantic Data Processing; Big Data Analysis; Big Data Programming Models; Big Data on Modern Hardware Systems; Big Data Applications; Enabling Big Data Technologies; Big Data Transaction Processing; Distributed Systems for Big Data; Big Data Security and Privacy

Business Process Analytics; Big Data Benchmarking; Graph data management and analytics; Data Compression; Big Stream Processing

Editorial Board:

Sherif Sakr (Editor-in-Chief), Institute of Computer Science, University of Tartu, Tartu, Estonia

Albert Y. Zomaya (Editor-in-Chief), School of Information Technologies, Sydney University, Sydney, Australia

Pramod Bhatotia, School of Informatics, University of Edinburgh, Edinburgh, UK

Rodrigo N. Calheiros, School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia

Aamir Cheema, Monash University, Australia

Jinjun Chen, School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, VIC, Australia

Philippe Cudré-Mauroux, eXascale Infolab, University of Fribourg, Fribourg, Switzerland

Marcos Dias de Assuncao, Inria, LIP, ENS Lyon, Lyon, France

Marlon Dumas, Institute of Computer Science, University of Tartu, Tartu, Estonia

Paolo Ferragina, Department of Computer Science, University of Pisa, Pisa, Italy

George Fletcher, Technische Universiteit Eindhoven, Eindhoven, Netherlands

Olaf Hartig, Linköping University, Linköping, Sweden

Bingsheng He, National University of Singapore, Singapore

Asterios Katsifodimos, TU Delft, Delft, Netherlands

Alessandro Margara, Politecnico di Milano, Milano, Italy

Kamran Munir, Computer Science and Creative Technologies, University of the West of England, Bristol, UK

Behrooz Parhami, Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA

Antonio Pescapè, Department of Electrical Engineering and Information Technology, University of Napoli Federico II, Napoli, Italy

Meikel Poess, Server Technologies, Oracle, Redwood Shores, California, United States

Deepak Puthal, Faculty of Engin

Presents 300+ entries covering key concepts and terms in the broad field of machine learning Updates and informs through in-depth essays and definitions, historical background, key applications, and bibliographies Supports quick and efficient discovery of information through extensive cross-references Opens the field to those inquiring into this fast-growing area of research Includes supplementary material: sn.pub/extras