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
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Generating spatiotemporal datasets on the WWW
ACM SIGMOD Record
Adaptive precision setting for cached approximate values
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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
IEEE Transactions on Knowledge and Data Engineering
Techniques for Efficient Road-Network-Based Tracking of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Change Tolerant Indexing for Constantly Evolving Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
AGILE: adaptive indexing for context-aware information filters
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A generic framework for monitoring continuous spatial queries over moving objects
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Indexing continuously changing data with mean-variance tree
Proceedings of the 2005 ACM symposium on Applied computing
Nearest and reverse nearest neighbor queries for moving objects
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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
Main-memory operation buffering for efficient R-tree update
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
The Bdual-Tree: indexing moving objects by space filling curves in the dual space
The VLDB Journal — The International Journal 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
Using Oracle Extensibility Framework for Supporting Temporal and Spatio-Temporal Applications
TIME '08 Proceedings of the 2008 15th International Symposium on Temporal Representation and Reasoning
A benchmark for evaluating moving object indexes
Proceedings of the VLDB Endowment
MobiQual: QoS-aware Load Shedding in Mobile CQ Systems
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient proximity detection among mobile users via self-tuning policies
Proceedings of the VLDB Endowment
Thread-level parallel indexing of update intensive moving-object workloads
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
MOVIES: indexing moving objects by shooting index images
Geoinformatica
Spatial indexing for massively update intensive applications
Information Sciences: an International Journal
Boosting moving object indexing through velocity partitioning
Proceedings of the VLDB Endowment
MOIST: a scalable and parallel moving object indexer with school tracking
Proceedings of the VLDB Endowment
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
| Hi-index | 0.00 |
The increased deployment of sensors and data communication networks yields data management workloads with update loads that are intense, skewed, and highly bursty. Query loads resulting from location-based services are expected to exhibit similar characteristics. In such environments, index structures can easily become performance bottlenecks. We address the need for indexing that is adaptive to the workload characteristics, called workload-aware, in order to cover the space in between maintaining an accurate index, and having no index at all. Our proposal, QU-Trade, extends R-tree type indexing and achieves workload-awareness by controlling the underlying index's filtering quality. QU-Trade safely drops index updates, increasing the overlap in the index when the workload is update-intensive, and it restores the filtering capabilities of the index when the workload becomes query-intensive. This is done in a non-uniform way in space so that the quality of the index remains high in frequently queried regions, while it deteriorates in frequently updated regions. The adaptation occurs online, without the need for a learning phase. We apply QU-Trade to the R-tree and the TPR-tree, and we offer analytical and empirical studies. In the presence of substantial workload skew, QU-Trade can achieve index update costs close to zero and can also achieve virtually the same query cost as the underlying index.