Big Data Analytics, Vol 33. Handbook of Statistics

  • ID: 3149020
  • Book
  • 390 Pages
  • Elsevier Science and Technology
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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.

  • Review of big data research challenges from diverse areas of scientific endeavor
  • Rich perspective on a range of data science issues from leading researchers
  • Insight into the mathematical and statistical theory underlying the computational methods used to address big data analytics problems in a variety of domains
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A. Modeling and Analytics 1. Document Informatics for Scientific Learning and Accelerated Discovery, Venu Govindaraju, Ifeoma Nwogu and Srirangaraj Setlur 2. An Introduction to Rare Event Simulation and Importance Sampling, Gino Biondini 3. A Large-scale Study of Language Usage as a Cognitive Biometric Trait, Neeti Pokhriyal, Ifeoma Nwogu and Venu Govindaraju 4. Customer Selection Utilizing Big Data Analytics, Jung Suk Kwac and Ram Rajagopal 5. Continuous Model Selection for Large-scale Recommender Systems, Simon Chan and Philip Treleaven 6. Zero-Knowledge Mechanisms for Private Release of Social Graph Summarization, Maryam Shoaran, Alex Thomo and Jens H. Weber 7. Distributed Confidence-Weighted Classification on Big Data Platforms, Nemanja Djuric, Slobodan Vucetic and Mihalo Grbovic B. Applications and Infrastructure 8. Big Data Applications in Health Sciences and Epidemiology, Saumyadipta Pyne, Madhav Marathe and Anil Kumar S. Vullikanti 9. Big Data Driven Natural Language Processing Research and Applications, Venkat N. Gudivada, Dhana Rao and Vijay V. Raghavan 10. Analyzing Big Spatial & Big Spatiotemporal Data: A Case Study of Methods and Applications, Varun Chandola, Ranga Raju Vatsavai, Devashish Kumar and Auroop Ganguly 11. Experimental Computational Simulation Environments for Socio-Economic-Financial Analytics, Michal Galas 12. Terabyte-Scale Image Similarity Search, Diana Moise and Denis Shestakov 13. Measuring Inter-Site Engagement in a Network of Sites, Janette Lehmann, Mounia Lalmas and Ricardo Baeza-Yates 14. Scaling RDF Triple Stores in Size and Performance: Modeling SPARQL Queries as Graph Homomorphism Routines, Vito Giovanni Castellana, Jesse Weaver, Alessandro Morari, Antonino Tumeo, David Haglin, John Thomas Feo and Oreste Villa
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