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
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Enhanced nearest neighbour search on the R-tree
ACM SIGMOD Record
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Performance of Nearest Neighbor Queries in R-Trees
ICDT '97 Proceedings of the 6th International Conference on Database Theory
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
Fast k most similar neighbor classifier for mixed data (tree k-MSN)
Pattern Recognition
Parallel k-most similar neighbor classifier for mixed data
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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The vast number of applications featuring multimedia and geometric data has made the R-tree a ubiquitous data structure in databases. A popular and fundamental operation on R-trees is nearest neighbor search. While nearest neighbor on R-trees has received considerable experimental attention, it has received somewhat less theoretical consideration. We study pruning heuristics for nearest neighbor queries on R-trees. Our primary result is the construction of non-trivial families of R-trees where k-nearest neighbor queries based on pessimistic (i.e. min-max) distance estimates provide exponential speedup over queries based solely on optimistic (i.e. min) distance estimates. The exponential speedup holds even when k = 1. This result provides strong theoretical evidence that min-max distance heuristics are an essential component to depth-first nearest-neighbor queries. In light of this, we also consider the time-space tradeoffs of depth-first versus best-first nearest neighbor queries and construct a family of R-trees where best-first search performs exponentially better than depth-first search even when depth-first employs min-max distance heuristics.