Computing iceberg queries efficiently using bitmap index positions

In this paper, we answers an iceberg query with minimum execution time by devising a new specialized index position algorithm. The iceberg queries are mainly intended to compute small outputs from large databases and or data warehouses provided on the user thresholds. The aggregate values are useful in computing knowledge which is delightful in taking part of the important decisions by an industry people such as knowledge workers, managers, and analysts in the field of decision support, information retrieval and knowledge discovery systems. The basic bitmap index technique offers a long execution time to compute the iceberg queries since it requires conducting of bitwise-AND operations between all pairs of bitmaps. Further, this execution time increases when the cardinality of an attribute increases. Therefore to quickly compute the iceberg queries, algorithm fetches the index positions of all 1bit from each bitmap vector in the bitmap table. Further, these indexed positions are processed to determine the common positions of the 1 bit between pair of bitmaps which answer as an iceberg query with minimum execution time. Exhaustive experimentation demonstrates our approach is much more efficient than existing strategy.