Big data analytics
by Govindaraju, Venugopal Raghavan, Vijay Rao, C. Radhakrishna, (Calyampudi Radhakrishna)
While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networ…
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- Full title :Big data analytics / edited by V. Govindaraju, V.V. Raghavan, C.R. Rao.
Summary
While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions.
Read More ↓Edition Statements
- Publication Date:©2015
Publication Statements
- Publisher(s):Elsevier Science Ltd
- Place of Publication:Amsterdam
- Language:eng
- Publication Date:©2015
The Physical Object
- Description:xvii, 372 pages : : illustrations ; : 24 cm
Edition Identifiers
- ISBN:0444634924, 9780444634924
- oclc:ocn906010369
- LC Classification:QA76.9.B45
- Dewey Decimal:006.312
Table of Contents
Part A: Modeling and Analytics -- Chapter 1: Document Informatics for Scientific Learning and Accelerated Discovery -- Chapter 2: An Introduction to Rare Event Simulation and Importance Sampling -- Chapter 3: A Large-Scale Study of Language Usage as a Cognitive Biometric Trait -- Chapter 4: Customer Selection Utilizing Big Data Analytics -- Chapter 5: Continuous model selection for large-scale recommender systems -- Chapter 6. Zero-knowledge mechanisms for private release of social graph summarization -- Chapter 7: Distributed confidence-weighted classification on big data platforms -- Part B: Applications and Infrastructure -- Chapter 8: Big data applications in health sciences and epidemiology -- Chapter 9: Big data driven natural language processing research and applications -- Chapter 10: Analyzing big spatial and big spatiotemporal data: a case study of methods and applications -- Chapter 11: Experimental computational simulation environments for big data analytic in social sciences -- Chapter 12: Terabyte-scale image similarity search -- Chapter 13: Measuring inter-site engagement in a network of sites -- Chapter 14: Scaling RDF triple stores in size and performance: modelin…
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