Data quality and the Internet of Things
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Zhang et al. and Liu et al. study literature on data quality based on quality issues, dimensions, and measures but exclude data quality techniques and their solution domain.
“For instance, Zhang et al. [156] and Liu et al. [110] study the literature on data quality based on quality issues, dimensions, and measures but exclude the data quality techniques (e.g., data repair) and their solution domain (i.e., the abstract environment where the data quality technique is developed).”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [110]
Liu et al. conducted an SLR on data quality in IoT based on 45 empirical studies from 2012 to 2018.
“Liu et al. [110] conducted an SLR on data quality in IoT based on 45 empirical studies from 2012 to 2018.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [110]
Liu et al.'s SLR is limited to data quality problems (issues), dimensions, and measures, and does not cover data quality techniques, their evaluation, application domains for data quality research, or software technologies for managing data quality.
“Contrary to our study, their SLR is limited to data quality problems (issues), dimensions, and measures.It does not cover topics such as data quality techniques, their evaluation, application domains for data quality research, and software technologies for managing data quality, which are the main points of our SLR.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [110]
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Data Quality Management in the Internet of Things
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A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
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Abdalzaher, M. S., Krichen, M., Shaaban, M. F., & Fouda, M. M. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3613669 · Cited by 10 · see citing works
“DQ plays a crucial role in IoT data management, which ensures the reliability and accuracy of information used in decision-making, processing, and engineering systems [55].”
- Authors
- Liu, C., Nitschke, P., Williams, S. P., & Zowghi, D.
- Affiliations
- University of Technology Sydney (AU)
- University of Koblenz and Landau (DE)
- Corresponding author
- Liu, Caihua
- Venue
- Computing
- Published
- 2019-07-30
- Volume
- 102
- Issue
- 2
- Pages
- 573-599
- ISSN
- 0010-485X (ISSN-L)
- 1436-5057
- OpenAlex
- W2965126084
- Cited by
- 71
- References
- 30
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 12 · 2023: 17 · 2022: 9 · 2021: 11 · 2020: 2
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computing (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things, data quality, data quality measurement, data quality dimensions, data quality issues, IoT devices, data quality management
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