Big Data Analytics

Erscheinungsjahr 2015
ISBN: 978-0-444-63492-4
Verlag: Elsevier Science & Technology

Buch, Englisch, 390 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 530 g

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.

Autoren/Hrsg.

Weitere Mitwirkende

Govindaraju, Venu

Raghavan, Vijay

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

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

Über Autor(innen)

Raghavan, Vijay
Dr. Vijay Raghavan is the Alfred and Helen Lamson/ BoRSF Endowed Professor in Computer Science at the Center for Advanced Computer Studies and the Director of the NSF-sponsored Industry/ University Cooperative Research Center for Visual and Decision Informatics. As the director, he co-ordinates several multi-institutional, industry-driven research projects and manages a budget of over $500K/year. From 1997 to 2003, he led a $2.3M research and development project in close collaboration with the USGS National Wetlands Research Center and with the Department of Energy's Office of Science and Technical Information on creating a digital library with data mining capabilities incorporated. His research interests are in data mining, information retrieval, machine learning and Internet computing. He has published over 250 peer-reviewed research papers- appearing in top-level journals and proceedings- that cumulatively accord him an h-index of 31, based on citations. He has served as major advisor for 24 doctoral students. Besides substantial technical expertise, Dr. Raghavan has vast experience managing interdisciplinary and multi- institutional collaborative projects. He has also directed industry-sponsored research, on projects pertaining to Neuro-imaging based dementia detection and literature-based biomedical hypotheses generation, respectively. He received the IEEE International Conference on Data Mining (ICDM) 2005 Outstanding Service Award. Dr. Raghavan serves as a member of the Executive Committee of the IEEE Technical Committee on Intelligent Informatics (IEEE-TCII), the Web Intelligence Consortium (WIC) Technical Committee and the Web Intelligence and Intelligent Agent Technology Conferences' Steering Committee. He was one of the Conference Co-Chairs of IEEE 2013 Big Data Conference. For many years of service to the community, he received the WIC 2013 Outstanding Service Award. He was a member of the Steering Committee of IEEE BigData 2014 conference held on Oct. 27 - 30, 2014 at Washington, D.C. He is one of the Editors-in-Chief of the Web Intelligence journal, an Associate Editor of the ACM Transactions on Internet Technology and the International J. of Computer Science & Applications, and a member of the International Rough Set Society Advisory Board. He is an ACM Distinguished Scientist and served as an ACM Distinguished Lecturer from 1993 - 2006. In addition, he served as a member of the Advisory Committee of the NSF Computer and Information Science and Engineering directorate (CISE-AC) during 2008 - 2010.

Govindaraju, Venu
Dr. Venu Govindaraju, SUNY Distinguished Professor of Computer Science and Engineering, is the Vice President of Research and Economic Development of the University at Buffalo and founding director of the Center for Unified Biometrics and Sensors. He received his Bachelor's degree with honors from the Indian Institute of Technology (IIT) in 1986, and his Ph.D. from UB in 1992. His research focus is on machine learning and pattern recognition in the domains of Document Image Analysis and Biometrics. Dr. Govindaraju has co-authored about 400 refereed scientific papers. His seminal work in handwriting recognition was at the core of the first handwritten address interpretation system used by the US Postal Service. He was also the prime technical lead responsible for technology transfer to the Postal Services in US, Australia, and UK. He has been a Principal or Co-Investigator of sponsored projects funded for about 65 million dollars. Dr. Govindaraju has supervised the dissertations of 30 doctoral students. He has served on the editorial boards of premier journals such as the IEEE Transactions on Pattern Analysis and Machine Intelligence and is currently the Editor-in-Chief of the IEEE Biometrics Council Compendium. Dr. Govindaraju is a Fellow of the ACM (Association of Computing Machinery), IEEE (Institute of Electrical and Electronics Engineers), AAAS (American Association for the Advancement of Science), the IAPR (International Association of Pattern Recognition), and the SPIE (International Society of Optics and Photonics). He is recipient of the 2004 MIT Global Indus Technovator award and the 2010 IEEE Technical Achievement award.