CFP last date
20 May 2024
Reseach Article

Reduce The Wastage of Data During Movement in Data Warehouse

by Ahmed Mateen, Lareab Chaudhary
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 152 - Number 8
Year of Publication: 2016
Authors: Ahmed Mateen, Lareab Chaudhary

Ahmed Mateen, Lareab Chaudhary . Reduce The Wastage of Data During Movement in Data Warehouse. International Journal of Computer Applications. 152, 8 ( Oct 2016), 20-24. DOI=10.5120/ijca2016911907

@article{ 10.5120/ijca2016911907,
author = { Ahmed Mateen, Lareab Chaudhary },
title = { Reduce The Wastage of Data During Movement in Data Warehouse },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 152 },
number = { 8 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016911907 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:57:39.074007+05:30
%A Ahmed Mateen
%A Lareab Chaudhary
%T Reduce The Wastage of Data During Movement in Data Warehouse
%J International Journal of Computer Applications
%@ 0975-8887
%V 152
%N 8
%P 20-24
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

In this research paper so as to handle Data in warehousing as well as reduce the wastage of data and provide a better results which takes more and more turn into a focal point of the data source business. Data warehousing and on-line analytical processing (OLAP) are vital fundamentals of resolution hold, which has more and more become a focal point of the database manufacturing. Lots of marketable yield and services be at the present accessible, and the entire primary database management organization vendor nowadays have contributions in the area assessment hold up spaces some quite dissimilar necessities on record technology compare to conventional on-line transaction giving out application. This article gives a general idea of data warehousing and OLAP technologies, with the highlighting on top of their latest necessities. So tools which is used for extract, clean-up and load information into back end of a information warehouse; multidimensional data model usual of OLAP; front end client tools for querying and data analysis; server extension for proficient query processing; and tools for data managing and for administration the warehouse. In adding to survey the circumstances of the art, this article also identify a number of capable research issue, a few which are interrelated to data wastage troubles. In this paper use some new techniques to reduce the wastage of data, provide better results. In this paper take some values, put in anova table and give results through graphs which shows performance.

  1. Schopf, J. M., Chervenak, A., Foster, I., Fraser, D., Gunter, D., LeRoy, N., and Tierney, B., 2007. End-to-end data solutions for distributed petascale science. Cyberinfrasructure technology Watch Quarterly, 3(4): 1-8.
  2. Klenz, Bradley W., and Donna Fulenwider, O., 2009. "The Quality Data Warehouse: Solving Problems for the Enterprise."Paper 142 in: Proceedings of the 24th SAS Users Group International Conference, SAS Institute Inc.1: 1-8.
  3. Kapdoskar, R., Gaonkar., S., Shelar., N., Surve, A., and Gavhane, S., 2015. Big Data Analytics. International Journal of Advanced Research in Computer and Communication Engineering, 4(10): 518-520.
  4. Krishnaveni, S. and Hemalatha, M., 2012. Analysis of Scheduling Algorithms in Data Warehouse for Query Processing, International Conference on Emerging Trends in Science, Engineering and Technology, 1: 109-114.
  5. Khatib, Y. E. and Edwards, C., 2007. A Survey-Based Study of Grid Traffic computing. In GridNets, Infrastructure, Service and Applications, 4: 1–4:8.
  6. Lin, Ma., Fengying Nie; Qian Lu .2015.“An analysis of supply chain restructuring based on Big Data and mobile Internet, A case study of warehouse-type supermarket”s. IEEE International Conference.
  7. Patel, A. B., Birla, M., and Nair. U., 2012. Addressing big data problem using Hadoop and Map Reduce. Engineering (NUiCONE), Nirma University International Conference on. IEEE, 1: 1-5.
  8. Patil, V. S. and Soni, P. D., 2011. Hadoop Skeleton and Fault Tolerance in Hadoop Clusters, Journal of Super computing, 56: 203-210.pdf.
  9. Hou, R., 2011. Research and analysis of data  warehouse technologies, Volume: 3 Computer Science and Network Technology (ICCSNT), International Conference on Pages: 1919-1922.
  10. VITRIA,. 2014. “The Operational Intelligence Company”,., accessed April IEEE International 22th
  11. Stonebraker, M., Brown. P., and Moore. D., 2013. Object-relational DBMSs, trackingthenext great wave. Morgan Kauffman Publishers, Inc., San Francisco, California, 2nd edition.
  12. Mann, S.,  Singh, B.  P., 2014. Empirical validation of multidimensional model for data warehouse, Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 3rd International Conference on IEEE Pages:  867-873.
  13. Tan W., Blake, M. B., and Dustdar, S., 2014. Social-network-sourced big data analytics. IEEE Internet Computing 5(1): 62-69.
  14. Weippl, E., Mangisengi, O., Essmayr, W., Lichtenberger F. and Winiwarter, W., 2009. An authorization model for data warehouses and OLAP. Workshop on Security in Distributed Data Warehousing, New Orleans. 1: 1-5.
  15. Tebourski, W., Kara, W. B. A. and Ghezela, R. B., 2014. New Data Warehouse Designing Approach Based on Principal Component Analysis. International Conference IEEE, 1-6.
  16. Thusoo, A. J., S. Sarma., N. Jain., Z. Shao., P. Chakka., N. Zhang., S. Antony., H. Liu. and R. Murthy. 2010. Hive – A Petabyte Scale Data Warehouse Using Hadoop. Stanford University Infolab,1, 1-10.
Index Terms

Computer Science
Information Sciences


Data warehouse IO OLAP analytical Hadoop volume velocity variety function Big Data.