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Evaluating the Quality of databases Instances based on Completeness and Accuracy of Data


Mohammed N. Al_Khwlani, Mariam Shmsan, Nadia Al-akhram


Vol. 13  No. 1  pp. 35-38


Data quality for database represents the most important of the essential things for any organization where these organizations depend on their databases for providing the required information in any situation or time. In addition, data quality directly influences every day important decisions that made on all management levels based on the data stored within databases. However, real-world databases often contain both syntactic and semantic errors that cause several problems and damages to the organization. This paper proposes an approach to evaluate the quality of databases instances for treating and cleaning the errors. The evaluation process is based on several criteria for data quality such as completeness and correctness. These criteria are measured by counting the problems of data items for each relation in databases in order to identify the relation that has the most number of problems. Detecting the noisy data values in this paper is made by the clustering approach, especially using the K-means algorithm. The proposed approach was applied on several samples of databases from different drivers of ODBC using an application system that is designed by java beans for this purpose.


data quality, database, clustering, noisy data