YANG, Qishan, Mouzhi GE and Markus HELFERT. Guildlines of Data Quality Issues for Data Integration in the Context of the TPC-DI Benchmark. In Proceedings of the 19th International Conference on Enterprise Information Systems. Porto, Portugal: SciTePress. p. 135-144. ISBN 978-989-758-247-9. doi:10.5220/0006334301350144. 2017.
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Basic information
Original name Guildlines of Data Quality Issues for Data Integration in the Context of the TPC-DI Benchmark
Authors YANG, Qishan (156 China), Mouzhi GE (156 China, guarantor, belonging to the institution) and Markus HELFERT (276 Germany).
Edition Porto, Portugal, Proceedings of the 19th International Conference on Enterprise Information Systems, p. 135-144, 10 pp. 2017.
Publisher SciTePress
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW Springer, indexed by SCOPUS, WoS, DBLP
RIV identification code RIV/00216224:14330/17:00096406
Organization unit Faculty of Informatics
ISBN 978-989-758-247-9
Doi http://dx.doi.org/10.5220/0006334301350144
UT WoS 000697605900013
Keywords in English Data integration; Data quality; ETL; TPC-DI benchmark
Tags firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 26/2/2018 13:03.
Abstract
Nowadays, many business intelligence or master data management initiatives are based on regular data integration, since data integration intends to extract and combine a variety of data sources, it is thus considered as a prerequisite for data analytics and management. More recently, TPC-DI is proposed as an industry benchmark for data integration. It is designed to benchmark the data integration and serve as a standardisation to evaluate the ETL performance. There are a variety of data quality problems such as multi-meaning attributes and inconsistent data schemas in source data, which will not only cause problems for the data integration process but also affect further data mining or data analytics. This paper has summarised typical data quality problems in the data integration and adapted the traditional data quality dimensions to classify those data quality problems. We found that data completeness, timeliness and consistency are critical for data quality management in data integration, and data consistency should be further defined in the pragmatic level. In order to prevent typical data quality problems and proactively manage data quality in ETL, we proposed a set of practical guidelines for researchers and practitioners to conduct data quality management in data integration.
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