Conference Proceeding

Multi-objective Query Optimization in Smartphone Social Networks.

Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
01/2011; DOI:10.1109/MDM.2011.37 In proceeding of: 12th IEEE International Conference on Mobile Data Management, MDM 2011, Luleå, Sweden, June 6-9, 2011, Volume 1
Source: DBLP

ABSTRACT The 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.

0 0
 · 
0 Bookmarks
 · 
50 Views
  • Source
    [show abstract] [hide abstract]
    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.
    Distributed and Parallel Databases 09/2012; · 0.81 Impact Factor

Full-text (3 Sources)

View
36 Downloads
Available from
Feb 28, 2013