A decision table method for randomness measurement
Alkharboush, Nawaf & Li, Yuefeng (2012) A decision table method for randomness measurement. In Watada, Junzo, Watanabe, Toyohide, & Philips-Wren, Gloria (Eds.) Intelligent Decision Technologies: Proceedings of the 4th International Conference on Intelligent Decision Technologies (IDT'2012) - Volume 1, Springer-Verlag , Japan, pp. 13-23.
Data quality has become a major concern for organisations. The rapid growth in the size and technology of a databases and data warehouses has brought significant advantages in accessing, storing, and retrieving information. At the same time, great challenges arise with rapid data throughput and heterogeneous accesses in terms of maintaining high data quality. Yet, despite the importance of data quality, literature has usually condensed data quality into detecting and correcting poor data such as outliers, incomplete or inaccurate values. As a result, organisations are unable to efficiently and effectively assess data quality. Having an accurate and proper data quality assessment method will enable users to benchmark their systems and monitor their improvement. This paper introduces a granules mining for measuring the random degree of error data which will enable decision makers to conduct accurate quality assessment and allocate the most severe data, thereby providing an accurate estimation of human and financial resources for conducting quality improvement tasks.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Conference Paper|
|Keywords:||Data mining, Quality assessment, Data warehouse|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science|
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 Springer|
|Deposited On:||10 Jul 2012 12:56|
|Last Modified:||16 Jan 2014 00:07|
Repository Staff Only: item control page