Abstract
Extracting small set of data from large or huge database is a challenge in front of data warehouse system. The queries executed on data warehouse are of the nature aggregation function followed by having clause. This type of query is called as iceberg query. Present database system executes it just like normal query so it takes more time to execute. To increase execution speed of iceberg query on large database is the challenge in front of researchers. Previous research uses tuple scan approach and bitmap index pruning strategy to execute query which is time-consuming and it faces the problem of fruitless bitwise AND-XOR operation. They focus on only COUNT and SUM aggregate functions. To address these problems and improve efficiency of iceberg query the proposed research makes use of tracking pointer concept. It avoids fruitless bitwise AND-XOR operations and also it minimizes the futile queue pushing problem that occurs in previous research. Along with COUNT and SUM function this study creates framework for MIN, MAX, COUNT, and SUM aggregate functions. This proposed work uniquely distinguishes the MIN, MAX, and SUM operations which is not found in existing systems.
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Prakash, K.S., Pratap, P.M.J. (2017). Tracking Pointer Based Approach for Iceberg Query Evaluation. In: Satapathy, S., Bhateja, V., Joshi, A. (eds) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 469. Springer, Singapore. https://doi.org/10.1007/978-981-10-1678-3_6
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DOI: https://doi.org/10.1007/978-981-10-1678-3_6
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