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Big Scientific Data Management

First International Conference, BigSDM 2018, Beijing, China, November 30 – December 1, 2018, Revised Selected Papers

  • Conference proceedings
  • © 2019

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Editors:
  1. Jianhui Li

    1. CNIC, CAS, Beijing, China

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  2. Xiaofeng Meng

    1. Renmin University of China, Beijing, China

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  3. Ying Zhang

    1. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

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  4. Wenjuan Cui

    1. CNIC, CAS, Beijing, China

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  5. Zhihui Du

    1. Tsinghua University, Beijing, China

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11473)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Included in the following conference series:

Conference proceedings info: BigSDM 2018.

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Table of contents (31 papers)

  1. Front Matter

    Pages i-xiii
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  2. Gravitational Waves: Detectors to Detections

    • Barry C. Barish
    Pages 1-5

  3. Big Data Challenges of FAST

    • Youling Yue, Di Li
    Pages 6-9

  4. CSSDC Big Data Processing and Applications in Space Science Missions

    • Yunlong Li, Jia Zhong, Fuli Ma, Ziming Zou
    Pages 10-15

  5. Information Services of Big Remote Sensing Data

    • Guojin He, Guizhou Wang, Tengfei Long, Huichan Liu, Weili Jiao, Wei Jiang et al.
    Pages 16-31

  6. Data Management in Time-Domain Astronomy: Requirements and Challenges

    • Chen Yang, Xiaofeng Meng, Zhihui Du, Zhiqiang Duan, Yongjie Du
    Pages 32-43

  7. AstroServ: A Distributed Database for Serving Large-Scale Full Life-Cycle Astronomical Data

    • Chen Yang, Xiaofeng Meng, Zhihui Du, JiaMing Qiu, Kenan Liang, Yongjie Du et al.
    Pages 44-55

  8. AstroBase: Distributed Long-Term Astronomical Database

    • Kenan Liang, Wei Guo, Lizhen Cui, Meng Xu, Qingzhong Li
    Pages 56-66

  9. An Efficient Parallel Framework to Analyze Astronomical Sky Survey Data

    • Xiaobin Ma, Zhihui Du, Yankui Sun, Andrei Tchernykh, Chao Wu, Jianyan Wei
    Pages 67-77

  10. Real-Time Query Enabled by Variable Precision in Astronomy

    • Yongjie Du, Xiaofeng Meng, Chen Yang, Zhiqiang Duan
    Pages 78-85

  11. Continuous Cross Identification in Large-Scale Dynamic Astronomical Data Flow

    • Zhiqiang Duan, Chen Yang, Xiaofeng Meng, Yongjie Du
    Pages 86-91

  12. Scientific Data Management and Application in High Energy Physics

    • Gang Chen, Yaodong Cheng
    Pages 92-104

  13. EventDB: A Large-Scale Semi-structured Scientific Data Management System

    • Wenjia Zhao, Yong Qi, Di Hou, Peijian Wang, Xin Gao, Zirong Du et al.
    Pages 105-115

  14. An Operator Library System for HEP Events Processing Based on Hbase Coprocessor

    • Zirong Du, Di Hou, Yong Qi
    Pages 116-124

  15. Event-Oriented Caching System for ROOT Data Analysis in HEP

    • Baoan Liu, Can Ma, Bing Li, Weiping Wang
    Pages 125-136

  16. Automated and Intelligent Data Migration Strategy in High Energy Physical Storage Systems

    • Zhenjing Cheng, Yaodong Cheng, Lu Wang, Qingbao Hu, Haibo Li, Qi Xu et al.
    Pages 137-145

  17. Using Hadoop for High Energy Physics Data Analysis

    • Qiulan Huang, Zhanchen Wei, Gongxing Sun, Yaodong Cheng, Zhenjing Cheng, Qingbao Hu
    Pages 146-153

  18. Cross-Domain Data Access System for Distributed Sites in HEP

    • Qi Xu, Zhenjing Cheng, Yaodong Cheng, Gang Chen
    Pages 154-164

  19. Multi-dimensional Index over a Key-Value Store for Semi-structured Data

    • Xin Gao, Yong Qi, Di Hou
    Pages 165-175

  20. Technology Dependency Graph (TDG): A Scientific Literature Mining Based Method for Technology Insight

    • Hui Gao, Wei Luo, Lin Gui, Ting Wang
    Pages 176-185
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  1. Big Scientific Data Management

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About this book

This book constitutes the refereed proceedings of the First International Conference on Big Scientific Data Management, BigSDM 2018, held in Beijing, Greece, in November/December 2018.

The 24 full papers presented together with 7 short papers were carefully reviewed and selected from 86 submissions. The topics involved application cases in the big scientific data management, paradigms for enhancing scientific discovery through big data, data management challenges posed by big scientific data, machine learning methods to facilitate scientific discovery, science platforms and storage systems for large scale scientific applications, data cleansing and quality assurance of science data, and data policies.

Editors and Affiliations

  • CNIC, CAS, Beijing, China

    Jianhui Li, Wenjuan Cui

  • Renmin University of China, Beijing, China

    Xiaofeng Meng

  • Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Ying Zhang

  • Tsinghua University, Beijing, China

    Zhihui Du

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