TY - CHAP
T1 - A Socialized System for Enabling the Extraction of Potential Values from Natural and Social Sensing
AU - Shinkuma, Ryoichi
AU - Sawada, Yasuharu
AU - Omori, Yusuke
AU - Yamaguchi, Kazuhiro
AU - Kasai, Hiroyuki
AU - Takahashi, Tatsuro
N1 - Publisher Copyright:
© 2015 Springer International Publishing Switzerland.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84975687918&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-09177-8_16
DO - 10.1007/978-3-319-09177-8_16
M3 - Chapter
AN - SCOPUS:84975687918
T3 - Modeling and Optimization in Science and Technologies
SP - 385
EP - 404
BT - Modeling and Optimization in Science and Technologies
PB - Springer Verlag
ER -