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.
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Shinkuma, R., Sawada, Y., Omori, Y., Yamaguchi, K., Kasai, H., Takahashi, T. (2015). A Socialized System for Enabling the Extraction of Potential Values from Natural and Social Sensing. In: Xhafa, F., Barolli, L., Barolli, A., Papajorgji, P. (eds) Modeling and Processing for Next-Generation Big-Data Technologies. Modeling and Optimization in Science and Technologies, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-09177-8_16
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DOI: https://doi.org/10.1007/978-3-319-09177-8_16
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