Continuous queries on trajectories of moving objects

Abstract

Since navigation systems and tracking devices are becoming ubiquitous in our daily life, the development of effcient methods for processing massive sets of mobile objects are of utmost importance. Although future routes of mobile objects are often known in advance in many applications, this information is not fully utilized in most methods so far. In this paper, we reveal the beneficial effects of exploiting future routes for the early generation of the expected results of spatio-temporal queries. This kind of probable results is important for operative analytics in many applications like smart eet management or intelligent logistics. For effciently computing the high number of future trajectory points, a new index structure is presented which allows for a fast maintenance of query results under continuous changes of mobile objects. Our methods make use of specific update patterns, which require substantially less maintenance costs than the most general case of an updat e. A set of experiments based on a commonly used simulation environment shows the efficiency of our approach. Copyrigh

Clustering spatial data streams for targeted alerting in disaster response

“…We focus on the task of online clustering of a stream of spatial data. Clustering and continuous queries on moving objects have also been dealt with in previous research [13,25]. However, these approaches require the availability of velocities of the objects and some guarantees on the minimum frequency of the location updates which are not available in CDR data.…”