Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Availability, Reliability, and Security

CD-ARES 2012: Multidisciplinary Research and Practice for Information Systems pp 233–243Cite as

  1. Home
  2. Multidisciplinary Research and Practice for Information Systems
  3. Conference paper
Distributed Sampling Storage for Statistical Analysis of Massive Sensor Data

Distributed Sampling Storage for Statistical Analysis of Massive Sensor Data

  • Hiroshi Sato21,
  • Hisashi Kurasawa21,
  • Takeru Inoue21,
  • Motonori Nakamura21,
  • Hajime Matsumura21 &
  • …
  • Keiichi Koyanagi22 
  • Conference paper
  • 1983 Accesses

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7465)

Abstract

Cyber-physical systems interconnect the cyber world with the physical world in which sensors are massively networked to monitor the physical world. Various services are expected to be able to use sensor data reflecting the physical world with information technology. Given this expectation, it is important to simultaneously provide timely access to massive data and reduce storage costs. We propose a data storage scheme for storing and querying massive sensor data. This scheme is scalable by adopting a distributed architecture, fault-tolerant even without costly data replication, and enables users to efficiently select multi-scale random data samples for statistical analysis. We implemented a prototype system based on our scheme and evaluated its sampling performance. The results show that the prototype system exhibits lower latency than a conventional distributed storage system.

Keywords

  • data accuracy
  • random sampling
  • relaxed durability

Download conference paper PDF

References

  1. Lee, E.A.: Cyber Physical Systems: Design Challenges. In: 2008 11th IEEE International Symposium on Object Oriented Real-Time Distributed Computing (ISORC), pp. 363–369 (2008)

    Google Scholar 

  2. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    CrossRef  Google Scholar 

  3. PostgreSQL, http://www.postgresql.org/

  4. Pgpool Wiki, http://www.pgpool.net/

  5. Arasu, A., Babcock, B., Babu, S., Cieslewicz, J., Datar, M., Ito, K., Motwani, R., Srivastava, U., Widom, J.: STREAM: The Stanford Data Stream Management System. Technical Report, Stanford InfoLab (2004)

    Google Scholar 

  6. Abadi, D., Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Erwin, C., Galvez, E., Hatoun, M., Hwang, J.H., Maskey, A., Rasin, A., Singer, A., Stonebraker, M., Tatbul, N., Xing, Y., Yan, R., Zdonik, S.: Aurora: A Data Stream Management System (Demonstration). In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2003 (2003)

    Google Scholar 

  7. Olken, F., Rotem, D., Xu, P.: Random sampling from hash files. In: Proc. SIGMOD 1990, pp. 375–386 (1989)

    Google Scholar 

  8. Olken, F., Rotem, D.: Random sampling from B+ trees. In: Proc. VLDB 1989, pp. 269–277 (1989)

    Google Scholar 

  9. Babcock, B., Chaudhuri, S., Das, G.: Dynamic sample selection for approximate query processing. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (SIGMOD 2003), pp. 539–550. ACM (2003)

    Google Scholar 

  10. Pol, A., Jermaine, C., Arumugam, S.: Maintaining very large random samples using the geometric file. The VLDB Journal 17(5), 997–1018 (2008)

    CrossRef  Google Scholar 

  11. Reeves, G., Nath, J.L.S., Zhao, F.: Managing massive time series streams with multi-scale compressed trickles. Proc. VLDB Endow. 2(1), 97–108 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NTT Network Innovation Laboratories, NTT Corporation, 3-9-11, Midori-cho, Musashino, Tokyo, Japan

    Hiroshi Sato, Hisashi Kurasawa, Takeru Inoue, Motonori Nakamura & Hajime Matsumura

  2. Faculty of Science and Engineering, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan

    Keiichi Koyanagi

Authors
  1. Hiroshi Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hisashi Kurasawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takeru Inoue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Motonori Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hajime Matsumura
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Keiichi Koyanagi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of IT, Engineering and Environment, University of South Australia, Mawson Lakes Campus, 5001, Adelaide, SA, Australia

    Gerald Quirchmayr

  2. Department of Information Technologies, University of Economics, W. Churchill Sq. 4, 130 67, Prague 3, Czech Republic

    Josef Basl

  3. School of Information Science, Korean Bible University, 16 Danghyun 2-gil, Nowon-gu, 139-791, Seoul, Korea

    Ilsun You

  4. Information Technology and Decision Sciences, Old Dominion University, 2076 Constant Hall, 23529, Norfolk, VA, USA

    Lida Xu

  5. Institute of Software Technology and Interactive Systems, Vienna University of Technology and SBA Research, Favoritenstrsse 9-11, 1040, Vienna, Austria

    Edgar Weippl

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 IFIP International Federation for Information Processing

About this paper

Cite this paper

Sato, H., Kurasawa, H., Inoue, T., Nakamura, M., Matsumura, H., Koyanagi, K. (2012). Distributed Sampling Storage for Statistical Analysis of Massive Sensor Data. In: Quirchmayr, G., Basl, J., You, I., Xu, L., Weippl, E. (eds) Multidisciplinary Research and Practice for Information Systems. CD-ARES 2012. Lecture Notes in Computer Science, vol 7465. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32498-7_18

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-32498-7_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32497-0

  • Online ISBN: 978-3-642-32498-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature