Abstract. By leveraging the capabilities of modern GPS-equipped mobile devices providing social-networking services, the interest in developing advanced services that combine location-based services with social networking services is growing drastically. Based on geo-social networks that couple personal location information with personal social context information, such services are facilitated by geo-social queries that extract useful information combining social relationships and current locations of the users. In this paper, we tackle the problem of geo-social skyline queries, a problem that has not been addressed so far. Given a set of persons D connected in a social network SN with information about their current location, a geo-social skyline query reports for a given user U ∈ D and a given location P (not necessarily the location of the user) the pareto-optimal set of persons who are close to P and closely connected to U in SN. We measure the social connectivity between users using the widely adoted, but very expensive Random Walk with Restart method (RWR) to obtain the social distance between users in the social network. We propose an efficient solution by showing how the RWR-distance can be bounded efficiently and effectively in order to identify true hits and true drops early. Our experimental evaluation shows that our presented pruning techniques allow to vastly reduce the number of objects for which a more exact social distance has to be computed, by using our proposed bounds only.
No abstract
This demonstration presents our Uncertain-Spatio-Temporal (UST) framework that we have developed in recent years. The framework allows not only to visualize and explore spatio-temporal data consisting of (location, time, object)-triples but also provides an extensive codebase easily extensible and customizable by developers and researchers. The main research focus of this UST-framework is the explicit consideration of uncertainty, an aspect that is inherent in spatio-temporal data, due to infrequent position updates, due to physical limitations and due to power constraints. The UST-framework can be used to obtain a deeper intuition of the quality of spatio-temporal data models. Such models aim at estimating the position of a spatio-temporal object at a time where the object's position is not explicitly known, for example by using both historic (traffic-) pattern information, and by using explicit observations of objects. The UST-framework illustrates the resulting distributions by allowing a user to move forward and backward in time. Additionally the framework allows users to specify simple spatio-temporal queries, such as spatio-temporal window queries and spatio-temporal nearest neighbor (NN) queries. Based on recently published theoretic concepts, the UST-framework allows to visually explore the impact of different models and parameters on spatio-temporal data. The main result showcased by the USTframework is a minimization of uncertainty by employing stochastic processes, leading to small expected distances between ground truth trajectories and modelled positions.
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