Trajectory Indexing Using Movement Constraints*

Abstract

With the proliferation of mobile computing, the ability to index efficiently the movements of mobile objects becomes important. Objects are typically seen as moving in two-dimensional (x, y) space, which means that their movements across time may be embedded in the three-dimensional (x, y, t) space. Further, the movements are typically represented as trajectories, sequences of connected line segments. In certain cases, movement is restricted; specifically, in this paper, we aim at exploiting that movements occur in transportation networks to reduce the dimensionality of the data. Briefly, the idea is to reduce movements to occur in one spatial dimension. As a consequence, the movement occurs in two-dimensional (x, t) space. The advantages of considering such lower-dimensional trajectories are that the overall size of the data is reduced and that lower-dimensional data is to be indexed. Since off-the-shelf database management systems typically do not offer higher-dimensional indexing, this reduction in dimensionality allows us to use existing DBMSes to store and index trajectories. Moreover, we argue that, given the right circumstances, indexing these dimensionality-reduced trajectories can be more efficient than using a three-dimensional index. A decisive factor here is the fractal dimension of the networkVthe lower, the more efficient is the proposed approach. This hypothesis is verified by an experimental study that incorporates trajectories stemming from real and synthetic road networks.

Efficient Maintenance of Continuous Queries for Trajectories

“…We are also working on incorporating some other optimization techniques such as shared memory buffer and data prefetching to improve the execution of the set of enabled triggers in the ORDBMS. Another interesting problem is how to utilize in our approach information of the underlying road networks [27] to further improve the efficiency of the reevaluation procedure.…”

Efficient MaxCount and threshold operators of moving objects

“…Civilis et al [7], [8] gave indexing methods that use networks, such as roads. Pfoser and Jensen [21] also considered spatiotemporal range queries in networks. Gupta et al [11] gave a technique for answering spatiotemporal range queries on objects that move along curves in a planar graph.…”

An efficient location encoding method for moving objects using hierarchical administrative district and road network

“…The method proposed in [16,17] uses the Hilbert curve [5] to transform two-dimensional locations into onedimensional representation. Since this method does not consider hierarchical administrative district during the transformation, it does not fit well the real world situations.…”