SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Indexing the past, present, and anticipated future positions of moving objects
ACM Transactions on Database Systems (TODS)
Efficient processing of past-future spatiotemporal queries
Proceedings of the 2006 ACM symposium on Applied computing
Supporting frequent updates in R-trees: a bottom-up approach
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Query and update efficient B+-tree based indexing of moving objects
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
ST2B-tree: a self-tunable spatio-temporal b+-tree index for moving objects
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Spatio-Temporal Indexing for Large Multimedia Applications
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Bs-tree: a self-tuning index of moving objects
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
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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.