Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Designing Access Methods for Bitemporal Databases
IEEE Transactions on Knowledge and Data Engineering
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Indexing Mobile Objects on the Plane
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Querying about the Past, the Present, and the Future in Spatio-Temporal Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Efficient indexing of the historical, present, and future positions of moving objects
Proceedings of the 6th international conference on Mobile data management
Indexing the past, present, and anticipated future positions of moving objects
ACM Transactions on Database Systems (TODS)
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
Hi-index | 0.00 |
Currently, most indexing methods of moving objects are focused on the past position, or the present and future one. In this paper, we propose a novel indexing method, called History TPR*-tree(HTPR*-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 of all objects. Based on the TPR*-tree, our Basic HTPR*-tree adds creation or update time of moving objects to leaf node entries. In order to improve the update performance, we present a bottom-up update strategy for the HTPR*-tree by supplementing a hash table, a bit vector and a direct access table. Experimental results show that the update performance of the HTPR*-tree is better than that of the Basic HTPR*-and TPR*-tree. In addition to support partial history queries, the update and predictive query performance of the HTPR*-tree are greatly improved compared with those of the RPPF-tree.