When Is Nearest Neighbors Indexable?

Abstract. In this paper, we consider whether traditional index structures are effective in processing unstable nearest neighbors workloads. It is known that under broad conditions, nearest neighbors workloads become unstable–distances between data points become indistinguishable from each other. We complement this earlier result by showing that if the workload for your application is unstable, you are not likely to be able to index it efficiently using (almost all known) multidimensional index structures. For a broad class of data distributions, we prove that these index structures will do no better than a linear scan of the data as dimensionality increases. Our result has implications for how experiments should be designed on index structures such as R-Trees, X-Trees and SR-Trees: Simply put, experiments trying to establish that these index structures scale with dimensionality should be designed to establish cross-over points, rather than to show that the methods scale to an arbitr...

• Type: Conference
• Year: 2005
• Where: ICDT
• Authors: Uri Shaft, Raghu Ramakrishnan
• Keywords: Database, ICDT 2005, Index Structures, Linear Scan, Nearest Neighbors Workloads