GE, Mouzhi, Stanislav CHREN, Bruno ROSSI and Tomáš PITNER. Data Quality Management Framework for Smart Grid Systems. Online. In Abramowicz W., Corchuelo R. Proceedings of the 22nd International Conference on Business Information Systems. Switzerland: Springer, 2019, p. 299-310. ISBN 978-3-030-20481-5. Available from: https://dx.doi.org/10.1007/978-3-030-20482-2_24.
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Basic information
Original name Data Quality Management Framework for Smart Grid Systems
Authors GE, Mouzhi (156 China, guarantor, belonging to the institution), Stanislav CHREN (703 Slovakia, belonging to the institution), Bruno ROSSI (380 Italy, belonging to the institution) and Tomáš PITNER (203 Czech Republic, belonging to the institution).
Edition Switzerland, Proceedings of the 22nd International Conference on Business Information Systems, p. 299-310, 12 pp. 2019.
Publisher Springer
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14330/19:00109300
Organization unit Faculty of Informatics
ISBN 978-3-030-20481-5
ISSN 1865-1348
Doi http://dx.doi.org/10.1007/978-3-030-20482-2_24
UT WoS 000490868400024
Keywords in English Smart grid; Data quality; Data quality problem; Smart meter
Tags core_B, firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 3/5/2020 11:14.
Abstract
New devices in smart grid such as smart meters and sensors have emerged to become a massive and complex network, where a large volume of data is flowing to the smart grid systems. Those data can be real-time, fast-moving, and originated from a vast variety of terminal devices. However, the big smart grid data also bring various data quality problems, which may cause the delayed, inaccurate analysis of results, even fatal errors in the smart grid system. This paper, therefore, identifies a comprehensive taxonomy of typical data quality problems in the smart grid. Based on the adaptation of established data quality research and frameworks, this paper proposes a new data quality management framework that classifies the typical data quality problems into related data quality dimensions, contexts, as well as countermeasures. Based on this framework, this paper not only provides a systematic overview of data quality in the smart grid domain, but also offers practical guidance to improve data quality in smart grids such as which data quality dimensions are critical and which data quality problems can be addressed in which context.
Links
EF16_019/0000822, research and development projectName: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur
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