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
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Extendible hashing—a fast access method for dynamic files
ACM Transactions on Database Systems (TODS)
Making B+- trees cache conscious in main memory
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Database Management Systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient Bulk Operations on Dynamic R-trees
ALENEX '99 Selected papers from the International Workshop on Algorithm Engineering and Experimentation
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Linear hashing: a new tool for file and table addressing
VLDB '80 Proceedings of the sixth international conference on Very Large Data Bases - Volume 6
Enhancing the B+-tree by dynamic node popularity caching
Information Processing Letters
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In many applications of similarity searching in databases, a set of similar queries appear more frequently. Since it is rare that a query point with its associated parameters (range or number of nearest neighbors) will repeat exactly, intelligent caching mechanisms are required to efficiently answer such queries. In addition, the performance of non-repeating and non-cached queries should not suffer too much either. In this paper, we propose RCached-tree, belonging to the family of R-trees, that aims to solve this problem. In every internal node of the tree up to a certain level, a portion of the space is reserved for storing popular queries and their solutions. For a new query that is encompassed by a cached query, this enables bypassing the traversal of lower levels of the subtree corresponding to the node as the answers can be obtained directly from the result set of the cached query. The structure adapts itself to varying query patterns; new popular queries replace the old cached ones that are not popular any more. Queries that are not popular as well as insertions, deletions and updates are handled in the same manner as in a general R-tree. Experiments show that the RCached-tree can outperform R-tree and other such structures by a significant margin when the proportion of popular queries is 20% or more by reserving 30-40% of the internal nodes as cache.