Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Data quality assessment from the user's perspective
Proceedings of the 2004 international workshop on Information quality in information systems
A Statistical Approach to Volume Data Quality Assessment
IEEE Transactions on Visualization and Computer Graphics
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Data quality has become increasingly important in information constructions and low data quality will influence the decision-making process related to design, operation, and management of hydrology application. Although many researches could be found that discuss data quality in many areas, few literature exist that particularly focuses on data quality in the field of hydrology. In this paper, we first analyze the key dimensions such as completeness, consistency and accuracy of hydrology date quality, and then propose an efficient date quality management framework based on those dimensions. Moreover, a general date quality assessment model to assess the data quality in these dimensions is also provided. At the end of paper, we proposed a series of methods and techniques to improve the data quality in hydrology database, and carried out in practice to prove it.