Distributed sampling storage for statistical analysis of massive sensor data

Hiroshi Sato, Hisashi Kurasawa, Takeru Inoue, Motonori Nakamura, Hajime Matsumura, Keiichi Koyanagi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMultidisciplinary Research and Practice for Information Systems - IFIP WG 8.4, 8.9/TC 5 Int. Cross-Domain Conference and Workshop on Availability, Reliability, and Security, CD-ARES 2012, Proceedings
Pages233-243
Number of pages11
DOIs
Publication statusPublished - 2012 Sep 6
EventInternational Cross-Domain Conference and Workshop on Availability, Reliability, and Security, CD-ARES 2012 - Prague, Czech Republic
Duration: 2012 Aug 202012 Aug 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7465 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Cross-Domain Conference and Workshop on Availability, Reliability, and Security, CD-ARES 2012
CountryCzech Republic
CityPrague
Period12/8/2012/8/24

Keywords

  • data accuracy
  • random sampling
  • relaxed durability

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Sato, H., Kurasawa, H., Inoue, T., Nakamura, M., Matsumura, H., & Koyanagi, K. (2012). Distributed sampling storage for statistical analysis of massive sensor data. In Multidisciplinary Research and Practice for Information Systems - IFIP WG 8.4, 8.9/TC 5 Int. Cross-Domain Conference and Workshop on Availability, Reliability, and Security, CD-ARES 2012, Proceedings (pp. 233-243). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7465 LNCS). https://doi.org/10.1007/978-3-642-32498-7_18