Partitioning Algorithms for the Computation of Average Iceberg Queries

Abstract

Iceberg queries are to compute aggregate functions over an attribute (or set of attributes) to find aggregate values above some specified threshold. It’s difficult to execute these queries because the number of unique data is greater than the number of counter buckets in memory. However, previous research has the limitation that average functions were out of consideration among aggregate functions. So, in order to compute average iceberg queries efficiently we introduce the theorem to select candidates by means of partitioning, and propose POP algorithm based on it. The characteristics of this algorithm are to partition a relation logically and to postpone partitioning to use memory efficiently until all buckets are occupied with candidates. Experiments show that proposed algorithm is affected by memory size, data order, and the distribution of data set.KeywordsExecution TimeMemory SizeMining Association RulePartitioning AlgorithmAggregate FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.