Evaluating Aggregate Functions of Iceberg Query Using Priority Based Bitmap Indexing Strategy

Kale Sarika Prakash, P.M. Joe Prathap

Abstract


Aggregate function and iceberg queries are important and common in many applications of data warehouse because users are generally interested in looking for variance or unusual patterns. Normally, the nature of the queries to be executed on data warehouse are the queries with aggregate function followed by having clause, these type of queries are known as iceberg query. Especially to have efficient techniques for processing aggregate function of iceberg query is very important because their processing cost is much higher than that of the other basic relational operations such as SELECT and PROJECT. Presently available iceberg query processing techniques faces the problem of empty bitwise AND,OR  XOR operation and requires more I/O access and time.To overcome these problems proposed research provides efficient algorithm to execute iceberg queries using priority based bitmap indexing strategy. Priority based approach consider  bitmap vector to be executed as per the priority.Intermediate results are evaluated to find probability of result.Fruitless operations are identified and skipped in advance which help to reduce I/O access and time.Time and iteration required to process query is reduced [45-50] % compare to previous strategy.  Experimental result proves the superiority of priorty based approach compare to previous bitmap processing approach.

Keywords


aggregate functions, bitmap index (BI), data warehouse (DW), iceberg query (IBQ), logical operations,

Full Text:

PDF


DOI: http://doi.org/10.11591/ijece.v7i6.pp3745-3752
Total views : 245 times


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

ISSN 2088-8708, e-ISSN 2722-2578