The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Guidelines for presentation and comparison of indexing techniques
ACM SIGMOD Record
Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
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
Benchmark Handbook: For Database and Transaction Processing Systems
Benchmark Handbook: For Database and Transaction Processing Systems
STAR-Tree: An Efficient Self-Adjusting Index for Moving Objects
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
DynaMark: A Benchmark for Dynamic Spatial Indexing
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Indexing of Moving Objects for Location-Based Services
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Approximate Selection Queries over Imprecise Data
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
Benchmarking access methods for time-evolving regional data
Data & Knowledge Engineering
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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
Main-memory operation buffering for efficient R-tree update
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A benchmark for evaluating moving object indexes
Proceedings of the VLDB Endowment
Trees or grids?: indexing moving objects in main memory
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
BerlinMOD: a benchmark for moving object databases
The VLDB Journal — The International Journal on Very Large Data Bases
Effectively indexing uncertain moving objects for predictive queries
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
An experimental analysis of iterated spatial joins in main memory
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
An infrastructure is emerging that enables the positioning of populations of on-line, mobile service users. In step with this, research in the management of moving objects has attracted substantial attention. In particular, quite a few proposals now exist for the indexing of moving objects, and more are underway. As a result, there is an increasing need for an independent benchmark for spatio-temporal indexes. This paper characterizes the spatio-temporal indexing problem and proposes a benchmark for the performance evaluation and comparison of spatio-temporal indexes. Notably, the benchmark takes into account that the available positions of the moving objects are inaccurate, an aspect largely ignored in previous indexing research. The concepts of data and query enlargement are introduced for addressing inaccuracy. As proof of concepts of the benchmark, the paper covers the application of the benchmark to three spatio-temporal indexes—the TPR-, TPR*-, and Bx-trees. Representative experimental results and consequent guidelines for the usage of these indexes are reported.