Efficient iceberg query evaluation using set representation

Iceberg query (IBQ) is a special class of aggregation query which compute aggregations upon user provided threshold (T). In data mining area, efficient evaluation of iceberg queries has been attracted by many researchers due to enormous production of data in industries and commercial sectors. In literature, different strategies were found for IBQ evaluation, but using compressed bitmap index technique provides efficient strategy among all. In this paper, we propose a new strategy for computing IBQ, which builds a set for each attribute value, contains its occurrences in the attribute column and performs set operations for producing result. An experimentation on synthetic dataset demonstrates our approach is efficient than existing strategies for lower thresholds.