Tracking Pointer and Look Ahead Matching Strategy to Evaluate Iceberg Driven Query

Abstract

Iceberg driven query is important and common in many applications of data mining and data warehousing. Main property of iceberg driven query is it extracts small set of data from huge database. It works on aggregation function followed by GROUP BY and HAVING clause. Due to involvement of aggregation function execution of iceberg driven query becomes tedious and complex work. Main objective of this research is to improve the performance of iceberg driven query by reducing the time, number of iteration and I/O access required to execute it. Currently available iceberg driven query processing technique faces the problems of empty bitwise AND, OR and XOR operation. Because of these problems they require more time and I/O access to execute query. To overcome above problems this research proposes tracking pointer and look ahead matching strategy to evaluate iceberg driven query. Tracking pointer will initiate the evaluation process as per the priority of vector. Look ahead matching strategy help to identify probable vector instead of generating one by one till the end of vector list. This strategy decides the probability of bitmap vector to be executed. Thus in advance it identifies and avoids unnecessary operations to be performed on bitmap vector. Our experimental result shows that time and number of iteration required to evaluate iceberg driven query using proposed approach is reduced [40 to 50] % even though data size increases. Thus we prove the effectiveness and efficiency of proposed approach to process iceberg driven query.

A Novel Approach to Design Distribution Preserving Framework for Big Data

“…The structured and unstructured forms data are included in the BD, which is a collection of a massive amount of data [27]. Consequently, it is a complicated task for the user to retrieve along with to recognize the appropriate data as of the larger amount of data [28]. Thus, to design the distribution preserving framework for BD, a methodology has been proposed utilizing MD-PAM and CG-ANN.…”