A probabilistic index structure for querying future positions of moving objects

We are witnessing a tremendous increase in internet connected, geo-positioned mobile devices, e.g., smartphones and personal navigation devices. Therefore, location related services are becoming more and more important. This results in a very high load on both communication networks and server-side infrastructure. To avoid an overload we point out the beneficial effects of exploiting future routes for the early generation of the expected results of spatio-temporal queries. Probability density functions are employed to model the uncertain movement of objects. This kind of probable results is important for operative analytics in many applications like smart fleet management or intelligent logistics. An index structure is presented which allows for a fast maintenance of query results under continuous changes of mobile objects. We present a cost model to derive initialization parameters of the index and show that extensive parallelization is possible. A set of experiments b ased on realistic data shows the efficiency of our approach.