Effective Use of Space for Pivot-Based Metric Indexing Structures

Among the metric space indexing methods, AESA is known to produce the lowest query costs in terms of the number of distance computations. However, its quadratic construction cost and space consumption makes it infeasible for large datasets. There have been some work on reducing the space requirements of AESA. Instead of keeping all the distances between objects, LAESA appoints a subset of the database as pivots, keeping only the distances between objects and pivots. Kvp uses the idea of prioritizing the pivots based on their distances to objects, only keeping pivot distances that it evaluates as promising. FQA discretizes the distances using a fixed amount of bits per distance instead of using system's floating point types. Varying the number of bits to produce a performance-space trade-off was also studied in Kvp. Recently, BAESA has been proposed based on the same idea, but using different distance ranges for each pivot. The t-spanner based indexing structure compacts the distance matrix by introducing an approximation factor that makes the pivots less effective. In this work, we show that the Kvp prioritization is oriented toward symmetric distance distributions. We offer a new method that evaluates the effectiveness of pivots in a better fashion by making use of the overall distance distribution. We also simulate the performance of our method combined with distance discretization. Our results show that our approach is able to offer very good space- performance trade-offs compared to AESA and tree-based methods.