Data construction method for the applications of workshop digital twin system
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High sampling frequency and the vast number of installed sensors cause duplicated data, missing values, and outliers.
“Kong et al. [18] focus on duplicated data, missing values, and outliers caused by high sampling frequency and the vast number of installed sensors.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [18]
Different studies employ different imputation techniques (median-based, mean-based, average-value, and matrix factorization) for missing value repair.
“Different studies employ different imputation techniques (median-based [17], mean-based [46], average-value [18], and matrix factorization [44]) for missing value repair.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [18]
Kong et al. discuss using data from similar sensors but do not provide any implementation.
“Kong et al. [18] discuss using data from similar sensors but do not provide any implementation.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [18]
Five primary studies [5,18,42,45,48] provide data cleaning techniques that address data noise.
“Five primary studies [5,18,42,45,48] provide data cleaning techniques that address data noise.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [18]
Only three data cleaning techniques [9,18,42] address high dimensionality.
“Only three data cleaning techniques [9,18,42] address high dimensionality.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [18]
Two techniques [9,18] employ the principal component analysis [53] to filter out irrelevant attributes.
“Two techniques [9,18] employ the principal component analysis [53] to filter out irrelevant attributes.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [18]
The three techniques are offline, and none of them provide online support for reducing high dimensions in input datasets for real-time ML applications.
“The three techniques are offline, and none of them provide online support for reducing high dimensions in input datasets for real-time ML applications.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [18]
The classification metrics include precision [40], recall [40], and accuracy [18,40].
“The former includes precision [40], recall [40], and accuracy [18,40].”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [18]
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A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
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A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
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- Authors
- Kong, T., Hu, T., Zhou, T., & Ye, Y.
- Affiliations
- Shandong University (CN)
- Qilu University of Technology (CN)
- Shandong Jianzhu University (CN)
- Corresponding author
- Hu, Tianliang
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 58
- Pages
- 323-328
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W3006802555
- Cited by
- 169
- References
- 24
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 45 · 2023: 41 · 2022: 21 · 2021: 23 · 2020: 11
- FWCI
- 15.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Manufacturing Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Manufacturing Process and Optimization
- Keywords
- digital twin, production workshop, dataset construction, production data, data representation, data organization, data management, functional requirements
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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