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
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient locking for concurrent operations on B-trees
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
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
STAR-Tree: An Efficient Self-Adjusting Index for Moving Objects
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
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
The R-Link Tree: A Recoverable Index Structure for Spatial Data
DEXA '94 Proceedings of the 5th International Conference on Database and Expert Systems Applications
Indexing of Moving Objects for Location-Based Services
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Main Memory Evaluation of Monitoring Queries Over Moving Objects
Distributed and Parallel Databases
SINA: scalable incremental processing of continuous queries in spatio-temporal databases
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
IEEE Transactions on Knowledge and Data Engineering
SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Indexing mobile objects using dual transformations
The VLDB Journal — The International Journal on Very Large Data Bases
Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Robust B+-Tree-Based Indexing of Moving Objects
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Relaxed space bounding for moving objects: a case for the buddy tree
ACM SIGMOD Record
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
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
Primal or dual: which promises faster spatiotemporal search?
The VLDB Journal — The International Journal on Very Large Data Bases
A benchmark for evaluating moving object indexes
Proceedings of the VLDB Endowment
Adaptive indexing of moving objects with highly variable update frequencies
Journal of Computer Science and Technology
Using compressed index structures for processing moving objects in large spatio-temporal databases
Journal of Systems and Software
Spatial indexing for massively update intensive applications
Information Sciences: an International Journal
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In a Moving Object Database (MOD), the dataset, for example, the location of objects and their distribution, and the workload change frequently. Traditional static indexes are not able to cope well with such changes, that is, their effectiveness and efficiency are seriously affected. This calls for the development of novel indexes that can be reconfigured automatically based on the state of the system. In this article, we design and present the ST2B-tree, a Self-Tunable Spatio-Temporal B+-tree index for MODs. In ST2B-tree, the data space is partitioned into regions of different density with respect to a set of reference points. Based on the density, objects in a region are managed using a grid of appropriate granularity; intuitively, a dense region employs a grid with fine granularity, while a sparse region uses a grid with coarse granularity. In this way, the ST2B-tree adapts itself to workload diversity in space. To enable online tuning, the ST2B-tree employs a “multitree” indexing technique. The underlying B+-tree is logically divided into two subtrees. Objects are dispatched to either subtree depending on their last update time. The two subtrees are rebuilt periodically and alternately. Whenever a subtree is rebuilt, it is tuned to optimize performance by picking an appropriate setting (e.g., the set of reference points and grid granularity) based on the most recent data and workload. To cut down the overhead of rebuilding, we propose an eager update technique to construct the subtree. Finally, we present a tuning framework for the ST2B-tree, where the tuning is conducted online and automatically without human intervention, and without interfering with the regular functions of the MOD. We have implemented the tuning framework and the ST2B-tree, and conducted extensive performance evaluations. The results show that the self-tuning mechanism minimizes the degradation of performance caused by workload changes without any noticeable overhead.