Geospatial Dataspaces: Location-based services and integration of User-Generated Content - Ευσταθιάδης, Χριστόδουλος (Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, 2016)
The technological advances during the past years have resulted in the change of how people use and interact with the internet. Novel Web technologies and resulting applications have lead to a participatory data ecosystem that when utilized properly will lead to more rewarding services. People have been using cell phones for social interaction, willingly producing vast amounts of information. This, in addition to the technological ability of cell phones to track locations have lead to the consideration of advanced location-based services that take into account not only the location but all of the user-generated data that is produced implicitly or explicitly. In this PhD thesis, we investigate the case of this kind of Location-based Services and specifically of how to improve the typical location-based queries with user-generated content taken from sources in the web in the form of texts. Towards this goal we have focused our research on three main directions: (a) Proposal of access methods that take advantage of the additional information taken from user-generated data, (b) proposal of new location-based queries on top of the proposed access methods that leverage the use of crowdsourced information, (c) efficiency of the execution of the queries leveraging the state of the art in geo-spatial algorithms, graph algorithms and research on set similarity. An extensive literature review has been made in for an in-depth research on the areas of interest towards this PhD thesis. New access methods and query processing algorithms have been proposed in order to support location-based services that are enriched with user-generated content that is extracted from web platforms. Experimental evaluation of the proposed algorithms and techniques prove their efficiency. A framework that works entirely within the database in order to support distance queries on large-scale graphs is proposed. Finally, we present a framework for the collection, analysis, querying and visualisation of social network data,that aims in the support for the proposed enhanced location-based services.