Many applications of high societal relevance -- e.g., transportation and traffic management, disaster remediation, location-aware social networking, (tourist) recommendation systems, military logistics (to name but a few) -- rely on some kind of Location Based Services (LBS). The crucial components to support such services, in turn, rely on efficient techniques for managing the data capturing the information pertaining to the whereabouts in time of the moving entities -- storing, retrieving and querying such data. Traditionally, such topics were subjects of the fields called Spatial/Spatio-Temporal Databases, Moving Objects Databases (MOD) and Geographic Information Systems (GIS) [2, 5, 11]. To give an intuitive idea about the magnitude -- according to Mc Kinsey survey from 2011 [9], the volume of location-in-time data exceeds the order of Peta-Bytes per year just from smartphones -- and this is only the "pure" GPS (Global Positioning System) data. Including the cell-towers location data would boost the size by two orders of magnitude -- however, this is not even close to the full magnitude of the variety of location-related data contained in numerous tweets and other social networks based communications (which is of interest for applications such as behavioral marketing).
['Goce Trajcevski']
Fusion of uncertain location data from heterogeneous sources