BOD-tree: An One-Dimensional Balanced Indexing Algorithm

Abstract

The rapid growth oftrajectory data has prompted researchers to develop multiple large trajectory data management systems. One of the fundamental requirements of all these systems, regardless of their architecture, is to partition data efficiently between machines. In the typical query operations of tracks, the query on ID is a frequent operation of track query, such as ID time range query, ID space range query, etc. A widely used ID indexing technique is to reuse an existing search tree, such as a Kd-tree, by building a temporary tree for the input samples and using its leaf nodes as partition boundaries. However, we show in this paper that this approach has significant limitations. To overcome these limitations, we propose a new indexing, BOD-tree, which inherits the main features of the Kd-tree and can also partition the dataset into multiple balanced splits. We test the method on real datasets, and extensive experiments show that our algorithm can improve resource usage efficiency.