Design and implement of hydrological data quality assessment system based on rules

2012 ◽  
Author(s):  
Li Chao ◽  
Zhou Xiaofeng ◽  
Zhou Hui
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Assessment System ◽  
Hydrological Data ◽  
Data Quality Assessment

Data quality assessment in hydrological information systems

2015 ◽  
Vol 17 (4) ◽  
pp. 640-661
Author(s):  
Li Chao ◽  
Zhou Hui ◽  
Zhou Xiaofeng
Keyword(s):  
Supply Chain ◽  
Data Quality ◽  
Quality Assessment ◽  
Assessment System ◽  
Assessment Model ◽  
Quality Of Data ◽  
Cognitive Differences ◽  
Hydrological Data ◽  
Low Performance

The hydrological data fed to hydrological decision support systems might be untimely, incomplete, inconsistent or illogical due to network congestion, low performance of servers, instrument failures, human errors, etc. It is imperative to assess, monitor and even control the quality of hydrological data residing in or acquired from each link of a hydrological data supply chain. However, the traditional quality management of hydrological data has focused mainly on intrinsic quality problems, such as outlier detection, nullity interpolation, consistency, completeness, etc., and could not be used to assess the quality of application – that is, consumed data in the form of data supply chain and with a granularity of tasks. To achieve these objectives, we first present a methodology to derive quality dimensions from hydrological information system by questionnaire and show the cognitive differences in quality dimension importance, then analyze the correlations between the tasks, classify them into five categories and construct the quality assessment model with time limits in the data supply chain. Exploratory experiments suggest the assessment system can provide data quality (DQ) indicators to DQ assessors, and enable authorized consumers to monitor and even control the quality of data used in an application with a granularity of tasks.

Research on Data Quality of E&P Database Base on Metadata-Driven Data Quality Assessment Architecture

2014 ◽  
Vol 530-531 ◽  
pp. 813-817
Author(s):  
Gang Huang ◽  
Xiu Ying Wu ◽  
Man Yuan ◽  
Rui Fang Li
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Evaluation System ◽  
Evaluation Criteria ◽  
Assessment System ◽  
Frame Structure ◽  
Quality Analysis ◽  
Systematic Evaluation ◽  
Data Quality Assessment ◽  
Definition Of

So far there have never been systematic evaluation criteria and entire assessment and evaluation system in Data quality internationally. Based on the research on the relative content of both international and domestic data quality, this article analyzes the requests of data quality for the large enterprises. First of all, this paper raises and builds a complete data quality assessment system. Second, the definitions and specific algorithms of data quality assessment indicators and poses data quality analysis are built to evaluate the architecture and processes. A frame structure of data quality meta-model is presented in this paper. In addition, this paper also designs an evaluation system. This system includes the classification and definition of data quality and the algorithm in evaluation index and the system and process of data quality evaluation. This paper provides credibility basis for enterprises in evaluation of data quality.

scholarly journals A Rule-Based Data Quality Assessment System for Electronic Health Record Data

2020 ◽  
Vol 11 (04) ◽  
pp. 622-634 ◽  
Author(s):  
Zhan Wang ◽  
John R. Talburt ◽  
Ningning Wu ◽  
Serhan Dagtas ◽  
Meredith Nahm Zozus
Keyword(s):  
Health Care ◽  
Data Quality ◽  
Quality Assessment ◽  
Health Care Facilities ◽  
Assessment System ◽  
Single Center ◽  
Rule Based ◽  
Data Errors ◽  
Data Quality Assessment ◽  
Care Facilities

Abstract Objective Rule-based data quality assessment in health care facilities was explored through compilation, implementation, and evaluation of 63,397 data quality rules in a single-center case study to assess the ability of rules-based data quality assessment to identify data errors of importance to physicians and system owners. Methods We applied a design science framework to design, demonstrate, test, and evaluate a scalable framework with which data quality rules can be managed and used in health care facilities for data quality assessment and monitoring. Results We identified 63,397 rules partitioned into 28 logic templates. A total of 819,683 discrepancies were identified by 4.5% of the rules. Nine out of 11 participating clinical and operational leaders indicated that the rules identified data quality problems and articulated next steps that they wanted to take based on the reported information. Discussion The combined rule template and knowledge table approach makes governance and maintenance of otherwise large rule sets manageable. Identified challenges to rule-based data quality monitoring included the lack of curated and maintained knowledge sources relevant to data error detection and lack of organizational resources to support clinical and operational leaders with investigation and characterization of data errors and pursuit of corrective and preventative actions. Limitations of our study included implementation within a single center and dependence of the results on the implemented rule set. Conclusion This study demonstrates a scalable framework (up to 63,397 rules) with which data quality rules can be implemented and managed in health care facilities to identify data errors. The data quality problems identified at the implementation site were important enough to prompt action requests from clinical and operational leaders.

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