Editors:
- Features state-of-the-art techniques for data management and analysis using current algorithms, models, and architecture
- Contains case studies describing how data analysis can improve the insurance, banking, e-commerce, biomedical, and oil industries
- Includes topics on education such as building repositories to support instructors and curriculum development in big data
Part of the book series: Lecture Notes in Social Networks (LNSN)
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Table of contents (14 chapters)
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Front Matter
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Back Matter
About this book
Various case studies included have been reported by data analysis experts who work closely with their clients in such fields as education, banking, and telecommunications. Understanding how data management has been adapted to these applications will help students, instructors and professionals in the field. Application areas also include the fields of social network analysis, bioinformatics, and the oil and gas industries.
Keywords
- dynamic social network
- fuzzy dynamic model
- social media prediction
- insurance fraud detection
- network visualization using genetic algorithm
- customer segmentation model
- live sentiment analysis
- data science and social networks
- big data architecture
- big data management
- data-driven science, modeling and theory building
- computational social sciences
Editors and Affiliations
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Department Electrical & Computer Engineering, University of Calgary, Calgary, Canada
Mohammad Moshirpour, Behrouz H. Far
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Department of Computer Science, University of Calgary, Calgary, Canada
Reda Alhajj
Bibliographic Information
Book Title: Applications of Data Management and Analysis
Book Subtitle: Case Studies in Social Networks and Beyond
Editors: Mohammad Moshirpour, Behrouz H. Far, Reda Alhajj
Series Title: Lecture Notes in Social Networks
DOI: https://doi.org/10.1007/978-3-319-95810-1
Publisher: Springer Cham
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-319-95809-5Published: 05 October 2018
Softcover ISBN: 978-3-030-07099-1Published: 31 January 2019
eBook ISBN: 978-3-319-95810-1Published: 04 October 2018
Series ISSN: 2190-5428
Series E-ISSN: 2190-5436
Edition Number: 1
Number of Pages: VIII, 217
Number of Illustrations: 19 b/w illustrations, 62 illustrations in colour
Topics: Big Data/Analytics, Big Data, Computational Intelligence, Data Mining and Knowledge Discovery, Data-driven Science, Modeling and Theory Building, Computational Social Sciences