Extending High-Dimensional Indexing Techniques Pyramid and iMinMax(θ): Lessons Learned
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.
Keywordshigh-dimensional indexing iMinMax Pyramid Technique
Unable to display preview. Download preview PDF.
- 6.Günnemann, S., Kremer, H., Lenhard, D., Seidl, T.: Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations. In: Proceedings of the 14th International Conference on Extending Database Technology, EDBT/ICDT 2011, pp. 237–248. ACM, New York (2011)Google Scholar
- 8.Ooi, B.C., Tan, K.-L., Yu, C., Bressan, S.: Indexing the edges – a simple and yet efficient approach to high-dimensional indexing. In: Proceedings of the Nineteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2000, pp. 166–174. ACM, New York (2000)CrossRefGoogle Scholar
- 9.Shi, Q., Nickerson, B.: Decreasing Radius K-Nearest Neighbor Search Using Mapping-based Indexing Schemes. Technical report, University of New Brunswick (2006)Google Scholar