Big Data Applications and Use Cases
This book presents different use cases in big data applications and related practical experiences. Many businesses today are increasingly interested in utilizing big data technologies for supporting their business intelligence so that it is becoming more and more important to understand the various practical issues from different practical use cases. This book provides clear proof that big data technologies are playing an ever increasing important and critical role in a new cross-discipline res…
Read More ↓Title Statements
- Full title :Big Data Applications and Use Cases.
Summary
This book presents different use cases in big data applications and related practical experiences. Many businesses today are increasingly interested in utilizing big data technologies for supporting their business intelligence so that it is becoming more and more important to understand the various practical issues from different practical use cases. This book provides clear proof that big data technologies are playing an ever increasing important and critical role in a new cross-discipline research between computer science and business.
Read More ↓Edition Statements
- Publication Date:2016.
Publication Statements
- Publisher(s):Imprint: Springer, Springer International Publishing
- Place of Publication:Cham
- Language:eng
- Publication Date:2016.
Notes
3.2 View Layer.
The Physical Object
- Description:1 online resource (216 pages)
Edition Identifiers
- ISBN:3319301446, 9783319301440, 9783319301464, 3319301462
- DOI:10.1007/978-3-319-30146-4
- oclc:ocn990741847
- LC Classification:QA75.5-76.95
- Dewey Decimal:004
Table of Contents
Introduction to Big Data; 1 Introduction to Big Data; 1.1 What is Big Data?; 1.2 Uses for Big Data; 1.2.1 Marketing; 1.2.2 Pervasive Computing; 1.2.3 Internet of Things (IoT); 1.2.4 Smart Cities; 2 Big Data Analytics; 3 Case Studies; 3.1 Healthcare; 3.2 Retail; 3.3 Insurance; 3.4 Google Flu Trends; 3.5 Telecommunications; 4 Security and Privacy Concerns; 4.1 Personal Information Protection and Electronic Documents Act (PIPEDA); 4.2 Personal Health Information Protection Act (PHIPA); 5 Conclusions; References.
A Bloom Filter-Based Approach for Supporting the Representation and Membership Query of Multidimensional Dataset1 Introduction; 2 Related Work; 3 CBF Design and Analysis; 3.1 CBF Algorithm and Structure; 3.1.1 CBF Structure; 3.1.2 Item Insertion; 3.1.3 Exact Membership Query; 3.1.4 By-Attribute Membership Query; 3.2 False Rate and Optimal Hash Number; 3.3 Space Efficiency and Time Complexity; 3.4 Growth of Dimensions; 4 Experimental Evaluation; 4.1 False Positive Rate and Optimal Hash Number; 4.2 Exact Membership Search; 4.3 By-Attribute Membership Search; 5 Conclusion and Future Work.
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