Quality Assurance in Knowledge Data Warehouse

Authors

  • Sri Haryati  Faculty of Computer Science, Universitas Pembangunan Panca Budi, Medan, Indonesia
  • Ali Ikhwan  Faculty of Science and Technology, Universitas Islam Negeri Sumatera Utara, Medan, Indonesia
  • Diki Arisandi  Faculty of Engineering, Department of Informatics, Universitas Abdurrab, Pekanbaru, Indonesia
  • Fadlina  Department of Informatics Management, AMIK STIEKOM, Medan, Indonesia
  • Andysah Putera Utama Siahaan  Ph.D. Student of Universiti Malaysia Perlis, Kangar, Malaysia

Keywords:

Data Mining, Knowledge, Data Warehouse

Abstract

Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision making for an organization. Combining multiple operational databases and external data create data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.

References

  1. M. J. Berry dan G. Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Support, New York: John Wiley & Sons, Inc, 1997.
  2. D. T. Larose, Data Mining Methods and Models, Canada: A John Wiley & Sons, Inc, 2006.
  3. C. D., Discovering Knowledge in Data: An Introduction to Data Mining, Canada: John Wiley & Sons, 2014.
  4. Zakea Il-Agure; Mr.Hicham Noureddine Itani, “LINK MINING PROCESS,” International Journal of Data Mining & Knowledge Management Process, vol. 7, no. 3, pp. 45-51, 2017.
  5. M. Mertik dan K. Dahlerup-Petersen, “Data engineering for the electrical quality assurance of the LHC - a preliminary study,” International Journal of Data Mining, Modelling and Management, vol. 9, no. 1, pp. 65 - 78, 2017.
  6. L. Nunez-Letamendia, J. Pacheco dan S. Casado, “Applying genetic algorithms to Wall Street,” International Journal of Data Mining, Modelling and Management, vol. 3, no. 4, pp. 319 - 340, 2011.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2017-08-31

Issue

Volume 3, Issue 6, July-August-2017

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sri Haryati, Ali Ikhwan, Diki Arisandi, Fadlina, Andysah Putera Utama Siahaan, " Quality Assurance in Knowledge Data Warehouse, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 3, Issue 6, pp.239-242, July-August-2017.