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dc.contributor.advisorSarkar, Riken
dc.contributor.advisorMarina, Maheshen
dc.contributor.authorKatsikouli, Panagiotaen
dc.date.accessioned2018-06-07T13:21:37Z
dc.date.available2018-06-07T13:21:37Z
dc.date.issued2018-07-02
dc.identifier.urihttp://hdl.handle.net/1842/31110
dc.description.abstractSmart-phones, wearables and mobile devices in general are the sensors of our modern world. Their sensing capabilities offer the means to analyze and interpret our behaviour and surroundings. When it comes to human behaviour, perhaps the most informative feature is our location and mobility habits. Insights from human mobility are useful in a number of everyday practical applications, such as the improvement of transportation and road network infrastructure, ride-sharing services, activity recognition, mobile data pre-fetching, analysis of the social behaviour of humans, etc. In this dissertation, we develop algorithms for processing mobility data. The analysis of mobility data is a non trivial task as it involves managing large quantities of location information, usually spread out spatially and temporally across many tracking sensors. An additional challenge in processing mobility information is to publish the data and the results of its analysis without jeopardizing the privacy of the involved individuals or the quality of the data. We look into a series of problems on processing mobility data from individuals and from a population. Our mission is to design algorithms with provable properties that allow for the fast and reliable extraction of insights. We present efficient solutions - in terms of storage and computation requirements - , with a focus on distributed computation, online processing and privacy preservation.en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.hasversionKatsikouli, P., Ghosh, A., and Sarkar, R. (2017). Density and privacy preserving publication of location datasets. INFOCOMen
dc.relation.hasversionKatsikouli, P., Sarkar, R., and Gao, J. (2014). Persistence based online signal and trajectory simpli cation for mobile devices. In Proceedings of the 22Nd ACM SIGSPATIAL International Conference on Advances in Geographic In- formation Systems, SIGSPATIAL '14, pages 371-380, New York, NY, USA. ACM.en
dc.relation.hasversionKatsikouli, P., Viana, A. C., Fiore, M., and Tarable, A. (2017). On the sampling frequency of human mobility. In Proceedings of the IEEE Global Communications Conference (GLOBECOM), 2017en
dc.relation.hasversionRadu, V., Katsikouli, P., Sarkar, R., and Marina, M. K. (2014). A semi-supervised learning approach for robust indoor-outdoor detection with smartphones. Proceeding of The 12th ACM Conference on Embedded Networked Sensor Systems (SenSys).en
dc.subjectlocation dataen
dc.subjectmobility dataen
dc.subjectanalysisen
dc.subjectalgorithmic solutionsen
dc.subjectsampling frequencyen
dc.subjectdistributed algorithmsen
dc.titleDistributed and privacy preserving algorithms for mobility information processingen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen
dc.rights.embargodate2100-12-31
dcterms.accessRightsRestricted Accessen


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