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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
M. Fang, N. Shivakumar, H. Garcia-Molina, R. Motwani, and J.D. Ullman, “Computing Iceberg Queries Efficiently,” Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 299–310, 1998.
G. Graefe, “Query Evaluation Techniques for Large Databases,” ACM Computing Surveys, vol. 25, no. 2, pp. 73–170, 1993.
W.P. Yan and P.A. Larson, “Data Reduction through Early Grouping,” Proc. Conf. Centre for Advanced Studies on Collaborative Research (CASCON), p. 74, 1994.
P.A. Larson, “Grouping and Duplicate Elimination: Benefits of Early Aggregation,” Technical Report MSR-TR-97-36, Microsoft Research, 1997.
J. Bae and S. Lee, “Partitioning Algorithms for the Computation of Average Iceberg Queries,” Proc. Second Int’l Conf. Data Warehousing and Knowledge Discovery (DaWaK), 2000.
A. Ferro, R. Giugno, P.L. Puglisi, and A. Pulvirenti, “BitCube: A Bottom-Up Cubing Engineering,” Proc. Int’l Conf. Data Warehousing and Knowledge Discovery (DaWaK), pp. 189–203, 2009.
Bin He, Hui-I Hsiao, Ziyang Liu, Yu Huang and Yi Chen, “Efficient Iceberg Query Evaluation Using Compressed Bitmap Index”, IEEE Transactions On Knowledge and Data Engineering, vol 24, issue 9, sept 2011, pp. 1570–1589.
C.V. Guru Rao, V. Shankar, “Efficient Iceberg Query Evaluation Using Compressed Bitmap Index by Deferring Bitwise- XOR Operations” 978-1-4673-4529-3/12/$31.00c 2012 IEEE.
C.V. Guru Rao, V. Shankar, “Computing Iceberg Queries Efficiently Using Bitmap Index Positions” DOI: 10.1190/ICHCI-IEEE.2013.6887811 Publication Year: 2013,Page(s): 1 – 6.
Vuppu.Shankar, Dr. C.V. Guru Rao, “Cache Based Evaluation of Iceberg Queries”, International conference on Computer and Communications Technologies (ICCCT), 2014, DOI: 10.1109/ICCCT2.2014.7066694,Publication Year: 2014, Page(s): 1–5.
Rao, V.C.S.; Sammulal, P., “Efficient iceberg query evaluation using set representation”, India Conference (INDICON), 2014 Annual IEEE DOI: 10.1109/INDICON.2014.7030537. Publication Year: 2014, Page(s): 1–5.
K.-Y. Whang, B.T.V. Zanden, and H.M. Taylor, “A Linear-Time Probabilistic Counting Algorithm for Database Applications,” ACM Trans. Database Systems, vol. 15, no. 2, pp. 208–229, 1990.
K.P. Leela, P.M. Tolani, and J.R. Haritsa, “On Incorporating Iceberg Queries in Query Processors” Proc. Int’l Conf. Database Systems for Advances Applications (DASFAA), pp. 431–442, 2004.
Ying Mei, Kaifan Ji*, Feng Wang, “A Survey on Bitmap Index Technologies for Large-scale Data Retrieval” 978-1-4799-2808-8/13 $26.00 © 2013 IEEE.
F. Delie`ge and T.B. Pedersen, “Position List Word Aligned Hybrid: Optimizing Space and Performance for Compressed Bitmaps,” Proc. Int’l Conf. Extending Database Technology (EDBT), pp. 228–239, 2010.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-10-1678-3_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1677-6
Online ISBN: 978-981-10-1678-3
eBook Packages: EngineeringEngineering (R0)