Efficient Trajectory Indexing in Moving Object Databases

Abstract

This paper addresses an index scheme capable of efficiently processing range queries in a large-scale trajectory database. We propose a new algorithm, which divides the time dimension into multiple time intervals and then, using this interval, classifies line segments that are to be indexed. Additionally, a supplementary index is built for the line segments within each time interval. This scheme supports a dramatic improvement in insert and search operation performance using the main memory index, in contrast to legacy schemes which store the index in disk space. Each time interval builds an index as follows: First, the extent of the spatial dimension is divided into multiple spatial cells to which the line segments are assigned evenly. We use a 2D-tree to maintain information on those cells. Then, for each cell, an additional 3D R*-tree is created on the spatio-temporal space (x, y, t). Such a multi-level indexing strategy alleviates the shortcomings of legacy schemes. Performance results obtained from intensive experiments show that our scheme enhances the performance of retrieve operations by 3 similar to 10 times, while using much less storage space.