We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

OLAP-based Decision Support System for Business Data Analysis

by Md. Geaur Rahman, Zannatul Ferdaus, Md. Nobir Uddin
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 18
Year of Publication: 2018
Authors: Md. Geaur Rahman, Zannatul Ferdaus, Md. Nobir Uddin
10.5120/ijca2018917854

Md. Geaur Rahman, Zannatul Ferdaus, Md. Nobir Uddin . OLAP-based Decision Support System for Business Data Analysis. International Journal of Computer Applications. 181, 18 ( Sep 2018), 1-7. DOI=10.5120/ijca2018917854

@article{ 10.5120/ijca2018917854,
author = { Md. Geaur Rahman, Zannatul Ferdaus, Md. Nobir Uddin },
title = { OLAP-based Decision Support System for Business Data Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 181 },
number = { 18 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number18/29960-2018917854/ },
doi = { 10.5120/ijca2018917854 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:06:17.237892+05:30
%A Md. Geaur Rahman
%A Zannatul Ferdaus
%A Md. Nobir Uddin
%T OLAP-based Decision Support System for Business Data Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 18
%P 1-7
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories in business organizations. In this paper, a novel approach of developing a decision support system using on-line analytical processing (OLAP) is presented. The OLAP application is optimal for data queries that do not change data. The system database is designed to promote: heavy indexing to improve query performance, denormalisation of the database to satisfy common query requirements and improve query response times, and use of a star-snowflake schema to organize the data within the database. The existing partitioning strategy is used to partition the fact table by time into equal segments, different-size segments and on different dimension of data warehouse, which provide the good query performance, optimize hardware performance, and simplify the management of the data warehouse by reducing the volume of data to satisfy a query. Entity-relation modeling is used to create a single complex model, which proven effective in creating efficient online transaction processing systems. In this system, cubes are used to organize and summarize data for efficient analytical querying. In addition, an enterprise information system model has also been presented in this paper in order to optimize the utilization of operational data for use strategically. The proposed system is evaluated on the cube data for the Rajshahi and Khulna regions for ten years from 1994 to 2003. Experimental results indicate the effectiveness of the proposed OLAP based Decision Support System.

References
  1. Rahman M. G. and Islam M. Z. (2016a): “Discretization of Continuous Attributes Through Low Frequency Numerical Values and Attribute Interdependency”, Expert Systems with Applications, Vol. 45, pp. 410 – 423.
  2. Rahman M. G. and Islam M. Z. (2016b): “Missing Value Imputation using a Fuzzy Clustering based EM Approach”, Knowledge and Information Systems, Vol. 46 (2), pp. 389 – 422.
  3. Rahman M. G. and Islam M. Z. (2011): “A Decision Tree-based Missing Value Imputation Technique for Data Pre-processing”, In Proc. of the Ninth Australasian Data Mining Conference (AusDM 11), Ballarat, Australia. December 01 – December 02, 2011, CRPIT, 121, pp. 41-50.
  4. Rahman M. G. (2018): “Reframing in Clustering: An Introductory Survey”, International Journal of Computer. 30(1): 32-42.
  5. Keen, P. (1980): "Decision support systems: a research perspective", Cambridge, Mass.: Center for Information Systems Research, Alfred P. Sloan School of Management.
  6. Keen, P. G. W. (1978): “Decision support systems: an organizational perspective”. Reading, Mass., Addison-Wesley Pub. Co. ISBN 0-201-03667-3.
  7. Henk, G. (1987): “Expert systems and artificial intelligence in decision support systems”, proceedings of the Second Mini Euroconference, Lunteren, The Netherlands, 17–20 November 1985. Springer, 1987. ISBN 90-277-2437-7. p.1-2.
  8. Power, D. J. (1996): “What is a DSS?”, The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
  9. Power, D. J. (2002): “Decision support systems: concepts and resources for managers”, Westport, Conn., Quorum Books.
  10. Sprague, R. H. and E. D. Carlson (1982): “Building effective decision support systems”, Englewood Cliffs, N.J., Prentice-Hall. ISBN 0-13-086215-0.
  11. Anahory, S. and Murray, D. (2001): “Data Warehouse in the Real World-A practical Guide for Building Decision Support System”, Pearson Education Asia, New Delhi.
  12. Award, E. M. (1999): “System Analysis and Design”, 2nd Edition, Galgotia Publications Ltd, New Delhi.
  13. Aptech Worldwide (2010): Implementing RDBMS Concepts with SQL Server 2000 -Aptech Worldwide.
  14. Efraim, T., Aronson, J. E. and Liang, T. P. (2008): “Decision Support Systems and Intelligent Systems”, p. 574.
  15. Adriaans, P. and Zantinge, D. (1999): “Data Mining”, Addison-Wesley, New Delhi.
  16. Gachet, A. (2004): “Building Model-Driven Decision Support Systems with Dicodess”, Zurich, VDF.
  17. OLAP (2017): Microsoft Analysis Server and OLAP Online Manual.
  18. Silberschatz, A., Korth, H. F. and Sudarshan, S. (1997): “Database System Concepts”, 3rd Edition, The McGraw-Hill Book Companies, New Delhi.
  19. Sprague, R. (1980): "A Framework for the Development of Decision Support Systems." MIS Quarterly. Vol. 4, No. 4, pp.1-25.
  20. Wright, A. and Sittig, D. (2008): "A framework and model for evaluating clinical decision support architectures ", Journal of Biomedical Informatics. 41: 982–990.
  21. Rahman M. G., Molla M. K. I., Siddique A. R. S. A. and Debnath R. C. (2005): “Developing A Decision Support System Using On-Line Analytical Processing (OLAP)”, In proc. of the 8th IEEE International Conference on Computer and Information Technology, Dhaka, Bangladesh, 28-30 December, 2005, vol no 8, pp. 293-298.
  22. Pressman, R. S. (1997): “Software Engineering - A Practitioner’s Approach”, 4th Edition, McGraw-Hill Book Companies, New Delhi.
  23. Rahman M. G. and Islam M. Z. (2014): “FIMUS: A Framework for Imputing Missing Values Using Co-Appearance, Correlation and Similarity Analysis”, Knowledge-Based Systems, Vol. 56, pp. 311 – 327.
  24. Rahman M. G. and Islam M. Z. (2013): “Missing Value Imputation Using Decision Trees and Decision Forests by Splitting and Merging Records: Two Novel Techniques”, Knowledge-Based Systems, Vol. 53, pp. 51 – 65.
  25. Han, J., Kamber, M., & Pei, J. (2006): “Data mining: concepts and techniques”. Morgan kaufmann.
  26. Mohamed R Alkotby, Elsaeed Elsaeed Mohamed Abd Elrazek and M Z Rashad (2018): “An Expert System to Diagnose and Fix Common Car Breakdowns for Industrial Technical Education in Egypt”, International Journal of Computer Applications. 182(7):30-37.
Index Terms

Computer Science
Information Sciences

Keywords

Data Analytics Dimension data DSS Fact Table OLAP

Powered by PhDFocusTM