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
Performance of B-tree concurrency control algorithms
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
The performance of current B-tree algorithms
ACM Transactions on Database Systems (TODS)
Communications of the ACM
Concurrency and recovery in generalized search trees
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Lock-free data structures
Efficient locking for concurrent operations on B-trees
ACM Transactions on Database Systems (TODS)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
High-Concurrency Locking in R-Trees
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Universal Constructions for Large Objects
WDAG '95 Proceedings of the 9th International Workshop on Distributed Algorithms
An Enhanced Concurrency Control Scheme for Multidimensional Index Structures
IEEE Transactions on Knowledge and Data Engineering
Design of vehicle information management system for effective retrieving of vehicle location
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
Multiversion concurrency control for multidimensional index structures
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
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Multi-dimensional index structures such as R-trees enable fast searching in high-dimensional spaces. They differ from uni-dimensional structures in the following aspects: (1) index regions in the tree may be modified during ordinary insert and delete operations, and (2) node splits during inserts are quite expensive. Both these characteristics may lead to reduced concurrency of update and query operations. In this paper, we examine how to achieve high concurrency for multi-dimensional structures. First, we develop a new technique for efficiently handling index region modifications. Then, we extend it to reduce/eliminate query blocking overheads during node-splits. We examine two variants of this extended scheme - one that reduces the blocking overhead for queries, and another that completely eliminates it. Experiments on image data on a shared-memory multiprocessor show that these schemes achieve up to 2 times higher throughput than existing techniques, and scale well with the number of processors.