A Density based Priority Queue Strategy to Evaluate Iceberg Queries Efficiently using Compressed Bitmap Indices

Abstract

In particular, iceberg query is a special class of aggregation query that computes aggregated values upon user interested threshold (T).The bitmap index is a common data structure for fast retrieval of matching tuples from data base table. These resultant tuples are useful to compute aggregations such as SUM, COUNT, AVG, MIN, MAX, and RANK. In this paper, we propose a density based bitmap pruning strategy to evaluate iceberg queries efficiently using compressed bitmap indices. The strategy prioritizes the vectors to be enter in to priority queue by allowing high density of 1's count that achieve optimal pruning effect. Extensive experimentation demonstrates our proposed approach is much more efficient than existing strategy.