cTraj: efficient indexing and searching of sequences containing multiple moving objects

Abstract

Indexing sequences containing multiple moving objects by all features of these objects captured at every clock tick results in huge index structures due to the large number of extracted features in all sampled instances. Thus, the main problems with current systems that index sequences containing multiple moving objects are: huge storage requirements for index structures, slow search time and low accuracy due to lack of representation of the time-varying features of objects. In this paper, a technique called cTraj to address these problems is proposed. For each object in a sequence, cTraj captures the features at sampled instances. Then, it maps the object's features at each sampled instance from high-dimensional feature space into a point in low-dimensional distance space. The sequence of points of an object in lowdimensional space is considered the time-varying feature trajectory of the object. To reduce storage requirements of an index structure, the sequence of points in each trajectory is represented by a minimum bounding box (MBB). cTraj indexes a sequence by the MBBs of its objects using a spatial access method (SAM), such as an R−tree; thus, greatly reducing storage requirements of the index and speeding up the search time. The cTraj technique does not result in any false dismissal, but the result might contain a few false alarms, which are removed by a two-step refinement process. The experiments show that the proposed cTraj technique produces effective results comparable to those of a sequential method, however much more efficient.

A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects

“…Since Guttman proposed the R-tree [7] in 1984, a plethora of indexing methods have been proposed in support of range queries as they are also related to different fields and disciplines [8,9] including for example spatiotemporal data [10]. However, the underlying assumption for most indices is that space is unconstrained and the distance between two objects can be calculated as the Euclidean distance [11][12][13][14]. In many applications, space is constrained and SOs are not uniformly distributed.…”