An iceberg query is a special class of an aggregation query that computes aggregate values above a user specified threshold. An Iceberg query evaluation using compressed bitmap index strategy is an efficient strategy, which prunes the bitmap vectors after performing bitwise-XOR operation. In this paper, we propose an effective bitmap pruning approach which further improves the efficiency of the strategy by deferring bitwise XOR operation. Exhaustive experimentation demonstrates our approach is much more efficient than existing strategy.

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Many data mining queries are basically identified as iceberg queries. Applications are required to be compute aggregate functions over an interesting attributes to find aggregate values above some specified threshold. Such queries are called as iceberg queries. We propose set operations instead of bitwise-AND operations to evaluate iceberg queries efficiently using very little memory and significantly fewer passes over data, as compared to current techniques that use Dynamic pruning approaches and Vector alignment algorithms. Set operations reduces the execution time and make evaluation process of Iceberg query very effective by reducing the number of bitmaps that are needed. The exhaustive experimentation gives better results than existing strategies.