Vol. 5 No. 2 (2021)
Articles

Avoid Re-computation of Target in Computing of Average Iceberg Queries

Published 2021-07-24

Abstract

Data analytics and data mining systems work on data stored in files, the files are not stored relationships among the data, from such kind of data we compute aggregate values over the set of required attributes for find insights of data, find attributes values which aggregation values greater than threshold. Such kind of queries called iceberg queries. Computing iceberg queries with average aggregate function are the default because of limited memory available. The existing method suffers from the re-computation of a candidate. We proposed a Record Traction Algorithm (RTA), it uses Domain partitioning theorem, it avoids re-computation of a candidate, It uses bit vector and bitmap numbers for avoid a candidates uniquely. our experiment reveals that our approach generates a candidate only once and input data will be reduced in further candidate sets generation so the Perfomence of proposed method improve.