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…
Read More ↓Title Statements
- Full title :Big data analytics / edited by Venu Govindaraju, Vijay 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
- Place of Publication:Amsterdam
- Language:eng
- Publication Date:©2015
The Physical Object
- Description:1 online resource (525 pages)
Edition Identifiers
- ISBN:9780444634979, 0444634975
- oclc:ocn916450225
- LC Classification:QA76.9.D343
- Dewey Decimal:006.3/12
Table of Contents
Front Cover; Big Data Analytics; Copyright; Contents; Contributors; Preface; Part A: Modeling and Analytics; Chapter 1: Document Informatics for Scientific Learning and Accelerated Discovery; 1. Introduction; 1.1 Sample Use Case; 1.1.1 Description; 1.1.2 Current Research Process; 1.1.3 Problems with the Current Process; 1.1.4 The Future Process; 1.1.5 Benefits of the Future Process; 2. How Document Informatics Will Aid Materials Discovery; 2.1 Motivation; 2.2 Big Data Justification; 2.3 Challenges of Meta-Learning in Materials Research; 3. The General Research Framework
4. Pilot Implementation4.1 Objective 1: To Design and Develop a Time-Based, Hierarchical Topic Model; 4.1.1 Problem Description; 4.1.2 Prior Work; 4.1.3 Research Contributions; 4.2 Objective 2: To Implement Algorithms for Extracting Text from x-y Plots and Tables; 4.2.1 Problem Description; 4.2.2 Prior Work; 4.2.3 Research Contributions; 4.3 Objective 3: To Develop an Interactive, Materials Network Visualization Tool; 4.3.1 Problem Description; 4.3.2 Prior Work; 4.3.3 Research Contributions; 4.4 Testing and Validation; References
Chapter 2: An Introduction to Rare Event Simulation and Importance Sampling1. Introd…
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Big Data Analytics, Volume 33 -- (.pdf)
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