Efficient Spatial Keyword Search in Trajectory Databases

An increasing amount of trajectory data is being annotated with text descriptions to better capture the semantics associated with locations. The fusion of spatial locations and text descriptions in trajectories engenders a new type of top-$k$ queries that take into account both aspects. Each trajectory in consideration consists of a sequence of geo-spatial locations associated with text descriptions. Given a user location $\lambda$ and a keyword set $\psi$, a top-$k$ query returns $k$ trajectories whose text descriptions cover the keywords $\psi$ and that have the shortest match distance. To the best of our knowledge, previous research on querying trajectory databases has focused on trajectory data without any text description, and no existing work has studied such kind of top-$k$ queries on trajectories. This paper proposes one novel method for efficiently computing top-$k$ trajectories. The method is developed based on a new hybrid index, cell-keyword conscious B$^+$-tree, denoted by \cellbtree, which enables us to exploit both text relevance and location proximity to facilitate efficient and effective query processing. The results of our extensive empirical studies with an implementation of the proposed algorithms on BerkeleyDB demonstrate that our proposed methods are capable of achieving excellent performance and good scalability.Comment: 12 page

Moving Object Trajectories Meta-Model And Spatio-Temporal Queries

In this paper, a general moving object trajectories framework is put forward to allow independent applications processing trajectories data benefit from a high level of interoperability, information sharing as well as an efficient answer for a wide range of complex trajectory queries. Our proposed meta-model is based on ontology and event approach, incorporates existing presentations of trajectory and integrates new patterns like space-time path to describe activities in geographical space-time. We introduce recursive Region of Interest concepts and deal mobile objects trajectories with diverse spatio-temporal sampling protocols and different sensors available that traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4, No.2, April 201

Querying Spatio-temporal Patterns in Mobile Phone-Call Databases

Abstract — Call Detail Record (CDR) databases contain millions of records with information about cell phone calls, including the position of the user when the call was made/received. This huge amount of spatiotemporal data opens the door for the study of human trajectories on a large scale without the bias that other sources (like GPS or WLAN networks) introduce in the population studied. Also, it provides a platform for the development of a wide variety of studies ranging from the spread of diseases to planning of public transport. Nevertheless, previous work on spatiotemporal queries does not provide a framework flexible enough for expressing the complexity of human trajectories. In this paper we present the Spatiotemporal Pattern System (STPS) to query spatiotemporal patterns in very large CDR databases. STPS defines a regular-expression query language that is intuitive and that allows for any combination of spatial and temporal predicates with constraints, including the use of variables. The design of the language took into consideration the layout of the areas being covered by the cellular towers, as well as “areas ” that label places of interested (e.g. neighborhoods, parks, etc) and topological operators. STPS includes an underlying indexing structure and algorithms for query processing using different evaluation strategies. A full implementation of the STPS is currently running with real, very large CDR databases on Telefónica Research Labs. An extensive performance evaluation of the STPS shows that it can efficiently find complex mobility patterns in large CDR databases. I