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dc.contributor.authorKonstantinidis, Andreasen
dc.contributor.authorZeinalipour-Yazdi, Constantinos D.en
dc.contributor.authorAndreou, Panayiotis G.en
dc.contributor.authorSamaras, George S.en
dc.creatorKonstantinidis, Andreasen
dc.creatorZeinalipour-Yazdi, Constantinos D.en
dc.creatorAndreou, Panayiotis G.en
dc.creatorSamaras, George S.en
dc.description.abstractThe bulk of social network applications for smart phones (e.g., Twitter, Face book, Foursquare, etc.) currently rely on centralized or cloud-like architectures in order to carry out their data sharing and searching tasks. Unfortunately, the given model introduces both data-disclosure concerns (e.g., disclosing all captured media to a central entity) and performance concerns (e.g., consuming precious smart phone battery and bandwidth during content uploads). In this paper, we present a novel framework, coined Smart Opt, for searching objects (e.g., images, videos, etc.) captured by the users in a mobile social community. Our framework, is founded on an in-situ data storage model, where captured objects remain local on their owner's smart phones and searches then take place over a novel lookup structure we compute dynamically, coined the Multi-Objective Query Routing Tree (MO-QRT). Our structure concurrently optimizes several conflicting objectives (i.e., it minimizes energy consumption, minimizes search delay and maximizes query recall), using a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) that calculates a diverse set of high quality non-dominated solutions in a single run. We assess our ideas with mobility patterns derived by Microsoft's Geolife project and social patterns derived by DBLP. Our study reveals that Smart Opt can yield query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors. © 2011 IEEE.en
dc.sourceProceedings - IEEE International Conference on Mobile Data Managementen
dc.source2011 12th IEEE International Conference on Mobile Data Management, MDM 2011en
dc.subjectMultiobjective optimizationen
dc.subjectSignal encodingen
dc.subjectMulti objectiveen
dc.subjectSocial Networksen
dc.subjectTrees (mathematics)en
dc.subjectEvolutionary algorithmsen
dc.subjectHigh qualityen
dc.subjectOrders of magnitudeen
dc.subjectInformation managementen
dc.subjectSocial networking (online)en
dc.subjectQuery routingen
dc.subjectEnergy utilizationen
dc.subjectIn-situ dataen
dc.subjectSmart phonesen
dc.subjectTelephone setsen
dc.subjectMulti objective evolutionary algorithmsen
dc.subjectLookup structuresen
dc.subjectSocial communitiesen
dc.subjectSocial patternsen
dc.subjectData Sharingen
dc.subjectMobility patternen
dc.subjectMulti-Objective Query Optimizationen
dc.subjectNondominated solutionsen
dc.subjectQuery optimizationen
dc.subjectSearching tasken
dc.subjectSmartphone Networksen
dc.subjectTelephone circuitsen
dc.titleMulti-objective query optimization in smartphone social networksen
dc.description.endingpage32 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors: IEEEen
dc.description.notesIEEE Computer Societyen
dc.description.notesCentre for Distance-Spanning Technology (CDT)en
dc.description.notesCENTRIA Research and Developmenten
dc.description.notesConference code: 87463en
dc.description.notesCited By :7</p>en
dc.contributor.orcidZeinalipour-Yazdi, Constantinos D. [0000-0002-8388-1549]
dc.contributor.orcidAndreou, Panayiotis G. [0000-0002-6369-1094]

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