On High Dimensional Indexing of Uncertain Data

In this paper, we will examine the problem of distance function computation and indexing uncertain data in high dimensionality for nearest neighbor and range queries. Because of the inherent noise in uncertain data, traditional distance function measures such as the
Lq-
metric and their probabilistic variants are not qualitatively effective. This problem is further magnified by the sparsity issue in high dimensionality. In this paper, we examine methods of computing distance functions for high dimensional data which are qualitatively effective and friendly to the use of indexes. In this paper, we show how to construct an effective index structure in order to handle uncertain similarity and range queries in high dimensionality. Typical range queries in high dimensional space use only a subset of the ranges in order to resolve the queries. Furthermore, it is often desirable to run similarity queries with only a subset of the large number of dimensions. Such queries are difficult to resolve with traditional index structures which use the entire set of dimensions. We propose query-processing techniques which use effective search methods on the index in order to compute the final results. We discuss the experimental results on a number of real and synthetic data sets in terms of effectiveness and efficiency. We show that the proposed distance measures are not only more effective than traditional Lq -norms, but can also be computed more efficiently over our proposed index structure.