Main-memory operation buffering for efficient R-tree update
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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
A benchmark for evaluating moving object indexes
Proceedings of the VLDB Endowment
Adaptive indexing of moving objects with highly variable update frequencies
Journal of Computer Science and Technology
Effectively indexing uncertain moving objects for predictive queries
Proceedings of the VLDB Endowment
Continuous online index tuning in moving object databases
ACM Transactions on Database Systems (TODS)
Irregularity in high-dimensional space-filling curves
Distributed and Parallel Databases
Using compressed index structures for processing moving objects in large spatio-temporal databases
Journal of Systems and Software
Boosting moving object indexing through velocity partitioning
Proceedings of the VLDB Endowment
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With the emergence of an infrastructure that enables the geo-positioning of on-line, mobile users, the management of so-called moving objects has emerged as an active area of research. Among the indexing techniques for efficiently answering predictive queries on moving-object positions, the recent Bx-tree is based on the B+-tree and is relatively easy to integrate into an existing DBMS. However, the Bx-tree is sensitive to data skew. This paper proposes a new query processing algorithm for the B^x-tree that fully exploits the available data statistics to reduce the query enlargement that is needed to guarantee perfect recall, thus significantly improving robustness. The new technique is empirically evaluated and compared with four other approaches and with the TPR-tree, a competitor that is based on the R*-tree. The results indicate that the new index is indeed more robust than its predecessor-it significantly reduces the number of I/O operations per query for the workloads considered. In many settings, the TPR-tree is outperformed as well.