On indexing mobile objects

Abstract

We show how to index mobile objects in one and two dimensions using efficient dynamic external memory data structures. The problem is motivated by real life applications in traffic monitoring, intelligent navigation and mobile communications domains. For the l-dimensional case, we give (i) a dynamic, external memory algorithm with guaranteed worst case performance and linear space and (ii) a practical approximation algorithm also in the dynamic, external memory setting, which has linear space and expected logarithmic query time. We also give an algorithm with guaranteed logarithmic query time for a restricted version of the problem. We present extensions of our techniques to two dimensions. In addition we give a lower bound on the number of I/O's needed to answer the d-dimensional problem. Initial experimental results and comparisons to traditional indexing approaches are also included.

Main Memory Evaluation of Monitoring Queries Over Moving Objects

“…In [16], trajectories are mapped to points in a higher-dimensional space which are then indexed. In [24], objects are indexed in their native environment with the index structure being parameterized with velocity vectors so that the index can be viewed at future times.…”

Video-streaming for fast moving users in 3G mobile networks

“…Efficient techniques for indexing moving objects in one and two dimensions are proposed in [12] while in [5] the trade-offs for indexing schemes to answer interval, rectangle, approximate nearest-neighbor, approximate farthest-neighbor and convex-hull queries are examined. The indexing of current and anticipated positions of moving objects in the context of location based services is examined in [25].…”

The Geometry of Uncertainty in Moving Objects Databases

“…A large body of work in moving objects databases has been concentrated on indexing in primal [17,22,20,21,28] or dual space [1,12,13]. [30,31] present specifications of what an indexing of moving objects needs to consider, and generation of spatial datasets for benchmarking data.…”

“…Largest efforts were made in the area of access methods. Aside from a purely spatial ( [8] surveys 50+ structures) and temporal databases [27], there are several recent results which tackle various problems of indexing spatio-temporal objects and dynamic attributes [1,12,13,17,22,28,30,31]. Representing and querying the location of moving objects as a function of time is introduced in [24], and the works in [36,37] address policies for updating and modeling imprecision and communication costs.…”