Evaluation of Iceberg Query Using Vector alignment

Authors

  • Mahalakshmi S Asst Professor, Dept.of Computer Science and Engineering , E.G.S Pillay Engineering College, Nagapattinam
  • Sowkarthika T Assistant Professor, Department of Computer Science and Engineering, E.G.S.Pillay Engineering College, Nagapattinam, Tamilnadu, India
  • Sindoori R Assistant Professor, Department of Computer Science and Engineering, E.G.S.Pillay Engineering College, Nagapattinam, Tamilnadu, India

Keywords:

Iceberg query, bitmap index, column - oriented database, dynamic pruning, vector alignment

Abstract

Modern computing system requires functionality that often computes aggregate values of interesting attributes by processing a huge amount of data in large databases. Iceberg query is one of the techniques which compute aggregate values in query which is an above user specified threshold. Here the threshold may represent the important and essential factor about the business insights. Usually iceberg query processing algorithm based on tuples scan based approach, which requires intensive disk access and computation, resulting in long pruning time especially when data size is large. The proposed system makes use of bitmap vector to perform query processing which occupies less space. It eliminates the entire databases scanning and processing to evaluate the query. It pruned unwanted processing and saves time and speed up the iceberg query processing significantly by using vector alignment algorithm.

References

Agrawal.R, Imielinski T., and Swami A.N., “Mining Association Rules between Sets of Items in Large Databases,” Proc.ACM SIGMOD Int’l Conf. Management of Data, pp. 207-216, 1993.

Bin He,Hui-I Hsiao,Ziyang Liu,Yu Huang and Yi Chen ―Efficient Iceberg Query Evaluation Using Compressed Bitmap Index , vol. 24,. NO.9, September 2012.

Chan C.Y.and Ioannidis Y.E., ”Bitmap Index Design and Evaluation,. -Proc.ACM SIGMOD int’I Conf. Management of Data,1998.

Fang M., Shivakumar N.,. Garcia-Molina H, Motwani R., and Ullman J.D., “Computing Iceberg Queries Efficiently,”Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 299-310, 1998

Ferro A, R.Giugno, P.L.Puglisi, and A.Pulvirenti,”Bitcube: ABottom-Up Cubing Enginerring, ”Proc. Int’l Conf. Data Warehousing and knowledge Discovery ( DaWak ), pp. 189-203, 2009.

Graefe G., Query Evaluation Techniques for Large Databases,” ACM Computing Surveys, vol.25, No.2, pp.73-170,1993.

Han J., Pei J.. Dong G., and Wanng, K.,”Efficient Computation of Iceberg Cubes with Complex Measures, Proc. ACMSIGMOD Int’l Conf.

Management of Data,pp1-12,2011.

Jrgens M, ”Tree Based Indexes versus Bitmap Indexes: A Performance study,” Pro.Int’l Workshop Design and Management of Data Warehouses (dmdw), 1999.

Larson P.-A,” Grouping and Duplicate Elimination: Benefits of Early Aggregation, ”Technical Report MSRTR-97-36, Microsoft Research, 1997.

Leela K.P, Tolani P.M., and Haritsa J.R.,”On Incorporating Iceberg Queries in Query Processors, ”Proc. Int’l conf. Database Systems for Advance Applications (DASFAA), pp.431-442,2004.

O’Neil P.E. and Graefe G., “Multi-Table Joins through Bitmapped Join Indices,” SIGMOD Record, vol. 24, no. 3, pp. 8-11, 1995.

Stockinger K, J. Cieslewicz, K. Wu, Rotem D., and Shoshani A., “Using Bitmap Index for Joint Queries on Structured and Text Data,” Annals of Information Systems, vol. 3, pp. 1-23, 2009

Wu K, Otoo E.J., and Shoshani A., “On the Performance of Bitmap Indices for High Cardinality Attributes,” Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 24-35, 2004.

Downloads

Published

2024-02-26

How to Cite

Mahalakshmi, S., Sowkarthika, T., & Sindoori, R. (2024). Evaluation of Iceberg Query Using Vector alignment. COMPUSOFT: An International Journal of Advanced Computer Technology, 3(06), 952–956. Retrieved from https://ijact.in/index.php/j/article/view/166

Issue

Section

Original Research Article

Similar Articles

<< < 10 11 12 13 14 15 

You may also start an advanced similarity search for this article.