Similarity search structures for metric data typically bound object partitions by ball regions. Since regions can overlap, a relevant issue is to estimate the proximity of regions in order to predict the number of objects in the regions' intersection. This paper analyzes the problem using a probabilistic approach and provides a solution that effectively computes the proximity through realistic heuristics that only require small amounts of auxiliary data. An extensive simulation to validate the technique is provided. An application is developed to demonstrate how the proximity measure can be successfully applied to the approximate similarity search. Search speedup is achieved by ignoring data regions whose proximity to the query region is smaller than a user-defined threshold. This idea is implemented in a metric tree environment for the similarity range and "nearest neighbors" queries. Several measures of efficiency and effectiveness are applied to evaluate proposed approximate search algorithms on real-life data sets. An analytical model is developed to relate proximity parameters and the quality of search. Improvements of two orders of magnitude are achieved for moderately approximated search results. We demonstrate that the precision of proximity measures can significantly influence the quality of approximated algorithms.

Signature files provide an efficient access method for text in documents, but retrieval is usually limited to finding documents that contain a specified Boolean pattern of words. Effective retrieval requires that documents with similar meanings be found through a process of plausible inference. The simplest way of implementing this retrieval process is to rank documents in order of their probability of relevance. In this paper techniques are described for implementing probabilistic ranking strategies with sequential and bit-sliced signature tiles and the limitations of these implementations with regard to their effectiveness are pointed out. A detailed comparison is made between signature-based ranking techniques and ranking using term-based document representatives and inverted files. The comparison shows that term-based representations are at least competitive (in terms of efficiency) with signature files and, in some situations, superior.