Main-memory operation buffering for efficient R-tree update
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Partition-based lazy updates for continuous queries over moving objects
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Towards efficient main-memory use for optimum tree index update
Proceedings of the VLDB Endowment
Data management challenges for computational transportation
Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
Trees or grids?: indexing moving objects in main memory
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Location-dependent query processing: Where we are and where we are heading
ACM Computing Surveys (CSUR)
An adaptive updating protocol for reducing moving object database workload
The VLDB Journal — The International Journal on Very Large Data Bases
Spatial indexing for massively update intensive applications
Information Sciences: an International Journal
Authentication of moving range queries
Proceedings of the 21st ACM international conference on Information and knowledge management
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Indexing moving objects is a fundamental issue in spatiotemporal databases. In this paper, we propose an adaptive Lazy-Update Grid-based index (LUGrid, for short) that minimizes the cost of object updates. LUGrid is designed with two important features, namely, lazy insertion and lazy deletion. Lazy insertion reduces the update I/Os by adding an additional memory-resident layer over the disk index. Lazy deletion reduces update cost by avoiding deleting single obsolete entry immediately. Instead, the obsolete entries are removed later by specially designed mechanisms. LUGrid adapts to object distributions through cell splitting and merging. Theoretical analysis and experimental results indicate that LUGrid outperforms former work by up to eight times when processing intensive updates, while yielding similar search performance.