International Journal of Computer Applications
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. The authors propose set operations instead of bit-wise 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.