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A Socialized System for Enabling the Extraction of Potential Values from Natural and Social Sensing

  • Ryoichi Shinkuma
  • Yasuharu Sawada
  • Yusuke Omori
  • Kazuhiro Yamaguchi
  • Hiroyuki Kasai
  • Tatsuro Takahashi
Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 4)

Abstract

This chapter tackles two problems we face when extracting values from sensing data: 1) it is hard for humans to understand raw/unprocessed sensing data and 2) it is inefficient in terms of management costs to keep all sensing data ‘usable’. This chapter also discusses a solution, i.e., the socialized system, which encodes the characteristics of sensing data in relational graphs so as to extract values that originally contained the sensing data from the relational graphs. The system model, the encoding/decoding logic, and the real-dataset examples are presented. We also propose a content distribution paradigm built on the socialized system that is called SocialCast. SocialCast can achieve load balancing, low-retrieval latency, and privacy while distributing content using relational metrics produced from the relational graph of the socialized system. We did a simulation and present the results to demonstrate the effectiveness of this approach.

Keywords

Network Graph Cache Size Physical Network Physical Link Cache Replacement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ryoichi Shinkuma
    • 1
  • Yasuharu Sawada
    • 1
  • Yusuke Omori
    • 1
  • Kazuhiro Yamaguchi
    • 2
  • Hiroyuki Kasai
    • 3
  • Tatsuro Takahashi
    • 1
  1. 1.Graduate School of InformaticsKyoto UniversityKyotoJapan
  2. 2.Kobe Digital Labo, IncKobeJapan
  3. 3.The University of Electro-communicationsChofuJapan

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