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
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
High-dimensional index structures database support for next decade's applications (tutorial)
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Indexing the edges—a simple and yet efficient approach to high-dimensional indexing
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
An Efficient Algorithm for the Approximate Median Selection Problem
CIAC '00 Proceedings of the 4th Italian Conference on Algorithms and Complexity
Querying high-dimensional data in single-dimensional space
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the 14th International Conference on Extending Database Technology
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Pyramid Technique and iMinMax(θ) are two popular high-dimensional indexing approaches that map points in a high-dimensional space to a single-dimensional index. In this work, we perform the first independent experimental evaluation of Pyramid Technique and iMinMax(θ), and discuss in detail promising extensions for testing k-Nearest Neighbor (kNN) and range queries. For datasets with skewed distributions, the parameters of these algorithms must be tuned to maintain balanced partitions. We show that, by using the medians of the distribution we can optimize these parameters. For the Pyramid Technique, different approximate median methods on data space partitioning are experimentally compared using kNN queries. For the iMinMax(θ), the default parameter setting and parameters tuned using the distribution median are experimentally compared using range queries. Also, as proposed in the iMinMax(θ) paper, we investigated the benefit of maintaining a parameter to account for the skewness of each dimension separately instead of a single parameter over all the dimensions.