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
On the complexity of approximating the maximal inscribed ellipsoid for a polytope
Mathematical Programming: Series A and B
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 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
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th 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
Computation of Minimum-Volume Covering Ellipsoids
Operations Research
On Khachiyan's algorithm for the computation of minimum-volume enclosing ellipsoids
Discrete Applied Mathematics
UserMap: an adaptive enhancing of user-driven XML-to-relational mapping strategies
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
An algorithm for separating patterns by ellipsoids
IBM Journal of Research and Development
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In this work an R-tree variant, which uses minimum volume covering ellipsoids instead of usual minimum bounding rectangles, is presented. The most significant aspects, which determine R-tree index structure performance, is an amount of dead space coverage and overlaps among the covering regions. Intuitively, ellipsoid as a quadratic surface should cover data more tightly, leading to less dead space coverage and less overlaps. Based on studies of many available R-tree variants (especially SR-tree), the eR-tree (ellipsoid R-tree) with ellipsoidal regions is proposed. The focus is put on the algorithm of ellipsoids construction as it significantly affects indexing speed and querying performance. At the end, the eR-tree undergoes experiments with both synthetic and real datasets. It proves its superiority especially on clustered sparse datasets.