Assisted Learning Virtual Support (ALVS) for postgraduate students, through the Graduate Virtual Research Environment (GVRE)
Robert Costello, Nigel Shaw
Abstract
Robert Costello, Nigel Shaw
Abstract
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.
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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.
Key concepts: Credibility, Computer science, Quality (philosophy), Frame (networking), Data quality, Process (computing), Architecture, Quality assessment