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Intelligent search in social communities of smartphone users

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Abstract

Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain, however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft’s GeoLife project, DBLP and Pics ‘n’ Trails but also using our real Android SmartP2P (http://smartp2p.cs.ucy.ac.cy/) system deployed over our SmartLab (http://smartlab.cs.ucy.ac.cy/) testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95 %, with one order of magnitude less time and two orders of magnitude less energy than its competitors.

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Notes

  1. “Location-Based Mobile Social Networking”, Market Development, Revenue Opportunities, Applications, and Key Industry Players, ABI Research3Q.

  2. “Google Apologizes for Buzz Privacy”, David Coursey, PC World Business Center (online), Feb. 15th, 2010.

  3. “Google and the Search for the Future”, Holman W. Jenkins Jr., The Wall Street Journal (online), Aug. 14th, 2010.

  4. “Customers Angered as iPhones Overload AT&T”, Jenna Wortham, The New York Times (online), Sept. 2nd, 2009.

  5. Without loss of generality we assume Boolean keyword queries over tags.

  6. The terms “solution”, “vector” and “QRT” are utilized interchangeably.

  7. Available at: http://smartp2p.cs.ucy.ac.cy/.

  8. Our system also supports command pipelining as opposed to utilizing separate connections for each step.

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Acknowledgements

This work was supported in part by the second author’s Startup Grant, funded by the University of Cyprus, EU’s FP7 CONET project, EU’s FP6 Marie Curie TOK “SEARCHiN” project and EU’s FP7 “MODAP” projects and US NSF IIS-10503. We would like to thank Mr. Christos Aplitsiotis for helping out with the development of SmartP2P and its experimentation on SmartLab.

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Correspondence to Andreas Konstantinidis.

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Communicated by: Dipanjan Chakraborty.

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Konstantinidis, A., Zeinalipour-Yazti, D., Andreou, P. et al. Intelligent search in social communities of smartphone users. Distrib Parallel Databases 31, 115–149 (2013). https://doi.org/10.1007/s10619-012-7108-0

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