HTPR*-Tree: An Efficient Index for Moving Objects to Support Predictive Query and Partial History Query

Abstract

Developing efficient index structures is an important issue for moving object database. Currently, most indexing methods of moving objects are focused on the past position, or on the present and future one. In this paper, we propose a novel indexing method, called HTPR*-tree (History Time-Parameterized R-tree), which not only supports predictive queries but also partial history ones involved from the most recent update instant of each object to the last update time. Based on the TPR*-tree, our HTPR*-tree adds creation or update time of moving objects to leaf node entries. This index is the foundation of indexing the past, present and future positions of moving objects. In order to improve the update performance, we present a bottom-up update strategy for the HTPR*-tree by supplementing three auxiliary structures which include hash index, bit vector, and direct access table. Experimental results show that the update performance of the HTPR*-tree is better than that of the TD_HTPR*- and TPR*-tree. Moreover, the HTPR*-tree can support partial history queries compared with TPR*-tree although the predictive query performance is a bit less.