Big Data in Astronomy
Scientific Data Processing for Advanced Radio Telescopes
- 1st Edition - June 13, 2020
- Imprint: Elsevier
- Editors: Linghe Kong, Tian Huang, Yongxin Zhu, Shenghua Yu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 9 0 8 4 - 5
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 9 0 8 5 - 2
Big Data in Radio Astronomy: Scientific Data Processing for Advanced Radio Telescopes provides the latest research developments in big data methods and techniques for radio ast… Read more
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Request a sales quoteBig Data in Radio Astronomy: Scientific Data Processing for Advanced Radio Telescopes provides the latest research developments in big data methods and techniques for radio astronomy. Providing examples from such projects as the Square Kilometer Array (SKA), the world’s largest radio telescope that generates over an Exabyte of data every day, the book offers solutions for coping with the challenges and opportunities presented by the exponential growth of astronomical data. Presenting state-of-the-art results and research, this book is a timely reference for both practitioners and researchers working in radio astronomy, as well as students looking for a basic understanding of big data in astronomy.
- Bridges the gap between radio astronomy and computer science
- Includes coverage of the observation lifecycle as well as data collection, processing and analysis
- Presents state-of-the-art research and techniques in big data related to radio astronomy
- Utilizes real-world examples, such as Square Kilometer Array (SKA) and Five-hundred-meter Aperture Spherical radio Telescope (FAST)
Practitioners and researchers working in data processing for astronomy; students studying data in astronomy
- Edition: 1
- Published: June 13, 2020
- No. of pages (Paperback): 438
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780128190845
- eBook ISBN: 9780128190852
LK
Linghe Kong
Linghe Kong is currently a Research Professor in Department of Computer Science and Engineering at Shanghai Jiao Tong University and an engineer in the scientific data processing group in SKA China. Before that, he was a postdoctoral researcher at Columbia University and McGill University. He received his Ph.D. degree from Shanghai Jiao Tong University, China, his Masters degree from TELECOM SudParis, France, and his B. E. degree from Xidian University, China. His research interests include big data, Internet of things, and mobile computing systems. He has published more than 60 papers in refereed journals and conferences, such as ACM MobiCom, IEEE INFOCOM, IEEE RTSS, IEEE ICDCS, IEEE TMC, and IEEE TPDS. He serves on the editorial boards of several journals including Springer Telecommunication Systems and KSII Transactions on Internet and Information Systems. He organized several special issues such as in IEEE Communications Magazine and in the Computer Journal. He is a senior member of IEEE.
Affiliations and expertise
Research Professor, Shanghai Jiao Tong University, ChinaTH
Tian Huang
Tian Huang is Research Associate of the Astrophysics Group, Cavendish Lab, University of Cambridge. He takes part in multiple radio telescope array projects and mainly focuses on data preprocessing and quality metrics. In March 2016, he graduated from the School of Microelectronics at Shanghai Jiao Tong University, where he completed his PhD thesis. His main research interest is Data Mining for time series, including time series big data indexing, anomaly detecting, and computer architecture for time series data mining and statistical models for time series data. He has published 9 SCI journal and 18 EI conference papers. He has rich experience on software and hardware co-designing.
Affiliations and expertise
Research Associate, Astrophysics Group, University of Cambridge, UKYZ
Yongxin Zhu
Yongxin Zhu is a full Professor at Shanghai Advanced Research Institute, Chinese Academy of Sciences (CAS). He is also an Adjunct Professor with the School of Microelectronics at the Shanghai Jiao Tong University (SJTU). He is currently the technical leader of Chinese Consortium of Science Data Processor (SDP) for Square Kilometre Array Telescope. He has published over 130 English journal and conference papers, 40 Chinese journal papers and 20 China patent approvals in the areas of computer architecture, embedded systems, and big data processing. With around 1,000 citations of these works in recent years, he has received recognition in China and Asia with IEEE best paper award, Shanghai innovation award, SJTU Annual Outstanding Teacher Award and Bilingual Teaching Award. To date, he has received around 20 million RMB in grants from various funding agencies and industrial partners in China. Prior to his tenure with CAS and SJTU, he worked as a research fellow with the National University of Singapore in 2002-2005, a senior consultant with S1 Incorporation (inventor of the 1st Internet banking in the world) in 1999-2002 and a teaching assistant with the Department of Computer Science and Engineering, SJTU in 1994-1995. He is a guest editor of Journal of Systems Architecture, senior member of IEEE and China Computer Federation (CCF). He has been also a Visiting Professor with National University of Singapore since 2013.
Affiliations and expertise
Professor, Shanghai Advanced Research Institute, Chinese Academy of Sciences, ChinaSY
Shenghua Yu
Shenghua Yu is an Associate Professor at National Astronomical Observatories, Chinese Academy of Sciences (CAS). He received his PhD degree in astrophysics from Queen’s University of Belfast in 2012, and worked as a post-doc fellow at University of Western Australia in 2014-2015. His main research interests include gravitational wave astrophysics and astronomy, double compact objects, radio emission from ultracool dwarfs and radiation mechanisms. He has published ~13 SCI journal papers, 3 Chinese journal and conference papers, 2 China patent approvals, and 3 China computer software copyrights in the research areas.
Affiliations and expertise
Associate Professor, National Astronomical Observatories, Chinese Academy of Sciences, ChinaRead Big Data in Astronomy on ScienceDirect