2008Unpublished venueRequires access

A Method for Measuring Data Quality in Data Integration

Lin Mo, Hua Zheng

Open publisher page 4 citations

Abstract

This paper reports our method on measuring data quality in data integration. Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. Data quality is crucial for operational data integration. We posit that data-integration need to handle the measure of data quality. So, measuring data quality in data integration is one of worthy research topics. This paper focuses on believability, a major aspect of quality. At first, the author analyzes the background and content of this paper, then description of dimensions of believability is given, and we present our approach for computing believability based on metadata, finally the summary and prospect are listed. In this method, we make explicit use of lineage-based measurements and develop a precise approach to measuring data quality.

About this research paper

What this paper is about

This paper reports our method on measuring data quality in data integration. Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. Data quality is crucial for operational data integration. We posit that data-integration need to handle the measure of data quality. So, measuring data quality in data integration is one of worthy research topics. This paper focuses on believability, a major aspect of quality. At first, the author analyzes the background and content of this paper, then description of dimensions of believability is given, and we present our approach for computing believability based on metadata, finally the summary and prospect are listed. In this method, we make explicit use of lineage-based measurements and develop a precise approach to measuring data quality.

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

This paper reports our method on measuring data quality in data integration. Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. Data quality is crucial for operational data integration. We posit that data-integration need to handle the measure of data quality. So, measuring data quality in data integration is one of worthy research topics. This paper focuses on believability, a major aspect of quality. At first, the author analyzes the background and content of this paper, then description of dimensions of believability is given, and we present our approach for computing believability based on metadata, finally the summary and prospect are listed. In this method, we make explicit use of lineage-based measurements and develop a precise approach to measuring data quality.

Key concepts: Metadata, Computer science, Data integration, Data quality, Quality (philosophy), Measure (data warehouse), Data mining, Data science

Related papers

Back to paper searchBrowse research topicsOriginal source
A Method for Measuring Data Quality in Data Integration — Research Paper | ScholarLens