A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Approximating block accesses in database organizations
Communications of the ACM
GHOST: Fine Granularity Buffering of Indexes
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Index Access with a Finite Buffer
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
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 the Current Positions of Moving Objects Using the Lazy Update R-tree
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Query Processing for Moving Objects with Space-Time Grid Storage Model
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Q+Rtree: Efficient Indexing for Moving Object Databases
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Frequent Update and Efficient Retrieval: an Oxymoron on Moving Object Indexes?
WISEW '02 Proceedings of the Third International Conference on Web Information Systems Engineering (Workshops) - (WISEw'02)
Unified Fine-Granularity Buffering of Index and Data: Approach and Implementation
ICCD '00 Proceedings of the 2000 IEEE International Conference on Computer Design: VLSI in Computers & Processors
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
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
Towards optimal utilization of main memory for moving object indexing
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Spatial indexing for massively update intensive applications
Information Sciences: an International Journal
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With the rapid advancement in wireless communications and positioning techniques, it is now feasible to track the positions of moving objects. However, existing indexes and associated algorithms, which are usually disk-based, are unable to keep up with the high update rate while providing speedy retrieval at the same time. Since main memory is much faster than disk, efficient management of moving-object database can be achieved through aggressive use of main memory. In this paper, we propose an integrated memory partitioning and activity conscious twin-index (IMPACT) framework where the moving object database is indexed by a pair of indexes based on the properties of the objects' movement-a main-memory structure manages active objects while a disk-based index handles inactive objects. As objects become active (or inactive), they dynamically migrate from one structure to the other. In the worst case that each time an object need to be migrated to the disk, which means each update may incur a disk access, the performance of IMPACT degrades to be the same as the disk-based index structures. Moreover, the main memory is also organized into two partitions-one for the main memory index, and the other as buffers for the frequently accessed nodes of the disk-based index. We also presented the detailed algorithms for different operations and a cost model to estimate the optimal memory allocation. Our analytical and experimental results show that the proposed IMPACT framework achieves significant performance improvement over the traditional indexing scheme.