Notification: Our email services are now fully restored after a brief, temporary outage caused by a denial-of-service (DoS) attack. If you sent an email on Dec 6 and haven't received a response, please resend your email.
CFP last date
20 December 2024
Reseach Article

A Peta-Scale Data Movement and Analysis in Data Warehouse (APSDMADW)

by Ahmed Mateen, Lareab Chaudhary
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 151 - Number 7
Year of Publication: 2016
Authors: Ahmed Mateen, Lareab Chaudhary
10.5120/ijca2016911702

Ahmed Mateen, Lareab Chaudhary . A Peta-Scale Data Movement and Analysis in Data Warehouse (APSDMADW). International Journal of Computer Applications. 151, 7 ( Oct 2016), 1-5. DOI=10.5120/ijca2016911702

@article{ 10.5120/ijca2016911702,
author = { Ahmed Mateen, Lareab Chaudhary },
title = { A Peta-Scale Data Movement and Analysis in Data Warehouse (APSDMADW) },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 151 },
number = { 7 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume151/number7/26242-2016911702/ },
doi = { 10.5120/ijca2016911702 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:56:26.426115+05:30
%A Ahmed Mateen
%A Lareab Chaudhary
%T A Peta-Scale Data Movement and Analysis in Data Warehouse (APSDMADW)
%J International Journal of Computer Applications
%@ 0975-8887
%V 151
%N 7
%P 1-5
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this research paper so as to handle Information warehousing as well as on-line synthetic dispensation OLAP are necessary aspects of conclusion support, which takes more and more turn into a focal point of the data source business. This paper offers an outline of information warehousing also OLAP systems with a highlighting on their latest necessities. All of us explain backside end tackle for extract, clean-up and load information into an Data warehouse; multidimensional data model usual of OLAP; front-end user tools for query and facts evaluation server extension for useful query dispensation; and apparatus for metadata managing and for supervision the stockroom. Insights centered on complete data on customer actions manufactured goods act and souk performance are powerful advance and opposition in the internet gap .In this research, conclude the company inspiration and the program and efficiency of server’s working in a data warehouse through use of some new techniques and get better and efficient results. Data in peta-byte scale. This test shows the data dropping rate in data warehouse. The locomotive is in creation at Yahoo! since 2007 and presently manages more than half a dozen peta bytes of data.

References
  1. Chen, Y., Alspaugh, S. and Katz, R., 2012. Interactive analytical processing in big data systems: A cross-industry study of mapreduce workloads. Proceedings of the VLDB Endowment, 5(12): 1802-1813.
  2. Surajit, C. and Dayal, U., 2011. An overview of data warehousing and OLAP technology. ACM Sigmod record, 26(1): 65-74.
  3. Das, T. K., and Mohapatro, A., 2014. A Study on Big Data Integration with Data Warehouse. International Journal of Computer Trends and Technology (IJCTT), 9(1): 188-192.
  4. Ji, C., Li., Y., Qiu., W., Awada, U., and Li, K., 2012. Big data processing in cloud computing environments. Pervasive Systems, Algorithms and Networks (ISPAN), 12th International Symposium on, IEEE, 1, 17-23.
  5. Ghazal, A., Rabl, T., Hu, M., Raab, F., Poess M., and Crolotte, A., 2013. BigBench: towards an industry standard benchmark for big data analytics. Proceedings of the ACM SIGMOD international conference on Management of data. ACM, 1: 1197-1208.
  6. Dumbill, E., 2014. What is Big Data? An Introduction to the Big Data, Landscape International Conference IEEE.
  7. Dean, J. and Ghemawat, S., 2014. MapReduce Simplified Data Processing on Large Clusters, Journal of Super computing, 65: 230-237.pdf.
  8. Hashem I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., and Khan, S. U., 2015. The rise of “big data” on cloud computing: Review and open research issues. Information Systems 47(1): 98-115.
  9. Silva, J. P., Santos, M. Y., and June, J. M. P., 2011. A Brief Survey on Online Analysis of Movement Data, 6th Iberian Conference on Information Systems and Technologies (CISTI).
  10. Kang, U., Tsourakakis, C. E. and Faloutsos, C., 2011. Pegasus: mining peta-scale graphs. Knowledge and information systems, 27(2): 303-325.
  11. Klenz, Bradley W., and Fulenwider, D. 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.
  12. Kaisler, S., Armour, F., Espinosa J. A. and Money, W., 2013. Big data: Issues and challenges moving forward. System Sciences (HICSS), 46th Hawaii International Conference on. IEEE,1: 995-104.
  13. Hey, T., Tansly, S., and Tolle, K., 2010. The Fourth Paradigm: Data- Intensive Scientific Discovery. Microsoft Research.
  14. Yang, L., and Fang, M., 2013. Study on the Method of Data Organization in Data Warehouse Based on Predicate Clustering. International Conference on Computer Science and Network Technology.
  15. Menon, A., 2012. Big data@ facebook. Proceedings of the workshop on Management of big data systems. ACM, 1: 31-32.
  16. Moniruzzaman, A. B. M., and Hossain, S. A., 2013. Nosql database: New era of databases for big data analytics-classification, characteristics and comparison. arXiv preprint arXiv:1307.0191 1: 1-14.
Index Terms

Computer Science
Information Sciences

Keywords

Data warehouse OLAP analytical Map Reduce volume MOPS VO CEDPS function processing