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
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing moving points (extended abstract)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-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
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Making the Pyramid Technique Robust to Query Types and Workloads
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Indexing the Trajectories of Moving Objects in Networks*
Geoinformatica
Indexing mobile objects using dual transformations
The VLDB Journal — The International Journal on Very Large Data Bases
Robust B+-Tree-Based Indexing of Moving Objects
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Modeling and querying moving objects in networks
The VLDB Journal — The International Journal 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
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
The V*-Diagram: a query-dependent approach to moving KNN queries
Proceedings of the VLDB Endowment
A benchmark for evaluating moving object indexes
Proceedings of the VLDB Endowment
Indexing Moving Objects Using Short-Lived Throwaway Indexes
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Update-efficient indexing of moving objects in road networks
Geoinformatica
Workload-aware indexing of continuously moving objects
Proceedings of the VLDB Endowment
Optimized algorithms for predictive range and KNN queries on moving objects
Information Systems
A highly optimized algorithm for continuous intersection join queries over moving objects
The VLDB Journal — The International Journal on Very Large Data Bases
RUM+-tree: a new multidimensional index supporting frequent updates
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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There have been intense research interests in moving object indexing in the past decade. However, existing work did not exploit the important property of skewed velocity distributions. In many real world scenarios, objects travel predominantly along only a few directions. Examples include vehicles on road networks, flights, people walking on the streets, etc. The search space for a query is heavily dependent on the velocity distribution of the objects grouped in the nodes of an index tree. Motivated by this observation, we propose the velocity partitioning (VP) technique, which exploits the skew in velocity distribution to speed up query processing using moving object indexes. The VP technique first identifies the "dominant velocity axes (DVAs)" using a combination of principal components analysis (PCA) and k-means clustering. Then, a moving object index (e.g., a TPR-tree) is created based on each DVA, using the DVA as an axis of the underlying coordinate system. An object is maintained in the index whose DVA is closest to the object's current moving direction. Thus, all the objects in an index are moving in a near 1-dimensional space instead of a 2-dimensional space. As a result, the expansion of the search space with time is greatly reduced, from a quadratic function of the maximum speed (of the objects in the search range) to a near linear function of the maximum speed. The VP technique can be applied to a wide range of moving object index structures. We have implemented the VP technique on two representative ones, the TPR*-tree and the Bx-tree. Extensive experiments validate that the VP technique consistently improves the performance of those index structures.