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資料種別 図書

Big data : techniques and technologies in geoinformatics

edited by Hassan A. Karimi

詳細情報

タイトル Big data : techniques and technologies in geoinformatics
著者 edited by Hassan A. Karimi
出版地(国名コード) US
出版地Boca Raton
出版社CRC Press, Taylor & Francis Group
出版年 2014
大きさ、容量等 xiv, 298 pages : illustrations, maps ; 24 cm
注記 ISBN : 9781466586512 (hardback : acidfree paper), 1466586516 (hardback : acidfree paper)
注記 Includes bibliographical references and index
ISBN 9781466586512
ISBN 1466586516
LCCN Permalinkへのリンク 2013047353
WorldCatへのリンク 859584195
部分タイトル Chapter 1. Distributed and parallel computing / Monir H. Sharker and Hassan A. Karimi -- chapter 2. GEOSS Clearinghouse : integrating geospatial resources to support the global earth observation system of systems / Chaowei Yang, Kai Liu, Zhenlong Li, Wenwen Li, Huayi Wu, Jizhe Xia, Qunying Huang, Jing Li, Min Sun, Lizhi Miao, Nanyin Zhou, and Doug Nebert -- chapter 3. Using a cloud computing environment to process large 3D spatial datasets / Ramanathan Sugumaran, Jeffrey Burnett, and Marc P. Armstrong -- chapter 4. Building open environments to meet big data challenges in earth sciences / Meixia Deng and Liping Di -- chapter 5. Developing online visualization and analysis services for NASA satellite-derived global precipitation products during the big geospatial data era / Zhong Liu, Dana Ostrenga, William Teng, and Steven Kempler -- chapter 6. Algorithmic design considerations for geospatial and/or temporal big data / Terence van Zyl --
部分タイトル chapter 7. Machine learning on geospatial big data / Terence van Zyl -- chapter 8. Spatial big data : case studies on volume, velocity, and variety / Michael R. Evans, Dev Oliver, Xun Zhou, and Shashi Shekhar -- chapter 9. Exploiting big VGI to improve routing and navigation services / Mohamed Bakillah, Johannes Lauer, Steve H.L. Liang, Alexander Zipf, Jamal Jokar Arsanjani, Amin Mobasheri, and Lukas Loos -- chapter 10. Efficient frequent sequence mining on taxi trip records using road network shortcuts / Jianting Zhang -- chapter 11. Geoinformatics and social media : new big data challenge / Arie Croitoru, Andrew Crooks, Jacek Radzikowski, Anthony Stefanidis, Ranga R. Vatsavai, and Nicole Wayant -- chapter 12. Insights and knowledge discovery from big geospatial data using TMC-pattern / Roland Assam and Thomas Seidl -- chapter 13. Geospatial cyberinfrastructure for addressing the big data challenges on the worldwide sensor web / Steve H.L. Liang and Chih-Yuan Huang --
部分タイトル chapter 14. OGC standards and geospatial big data / Carl Reed
出版年月日等 [2014]
出版年月日等 ©2014
件名(キーワード) Geography--Data processing
件名(キーワード) Big data
件名(キーワード) Geographic information systems
件名(キーワード) Geospatial data
件名(キーワード) High performance computing
件名(キーワード) MATHEMATICS--General
件名(キーワード) TECHNOLOGY & ENGINEERING--Remote Sensing & Geographic Information Systems
件名(キーワード) TECHNOLOGY & ENGINEERING--Telecommunications
NDLC G81
LCC G70.2
DDC 910.285/57
要約・抄録 "Preface What is big data? Due to increased interest in this phenomenon, many recent papers and reports have focused on defining and discussing this subject. A review of these publications would point to a consensus about how big data is perceived and explained. It is widely agreed that big data has three specific characteristics: volume, in terms of large-scale data storage and processing; variety, or the availability of data in different types and formats; and velocity, which refers to the fast rate of new data acquisition. These characteristics are widely referred to as the three Vs of big data, and while projects involving datasets that only feature one of these Vs are considered to be big, most datasets from such fields as science, engineering, and social media feature all three Vs. To better understand the recent spurt of interest in big data, I provide here a new and different perspective on it. I argue that the answer to the question of "What is big data?" depends on when the question is asked, what application is involved, and what computing resources are available. In other words, understanding what big data is requires an analysis of time, applications, and resources. In light of this, I categorize the time element into three groups: past (since the introduction of computing several decades ago), near-past (within the last few years), and present (now). One way of looking at the time element is that, in general, big data in the past meant dealing with gigabyte-sized datasets, in the near-past, terabyte-sized datasets, and in the present, petabyte-sized datasets. I also categorize the application element into three groups: scientific (data used for complex modeling, analysis, and simulation), business (data used for business analysis and modeling), and general"--
対象利用者 一般
資料の種別 図書
言語(ISO639-2形式) eng : English

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