The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
A three level hierarchical architecture for an efficient storage of industry 4.0 data
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Four of them [5,42,45,48] use filtering to clean noise.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [42]
In the citing paper
“Half of the data cleaning techniques [8,14,29,42,43,45,49] in our SLR have not been evaluated.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [42]
In the citing paper
“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 [42]
In the citing paper
“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 [42]
In the citing paper
“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 [42]
In the citing paper
“Five primary studies [1,14,26,42,44] focus on data quality management in the Cloudbased architecture layers (L4, L5, and L6).”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [42]
In the citing paper
“Three of these studies [14,42,44] propose data quality management techniques (data repair and cleaning) running on the Cloud.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [42]
In the citing paper
“These techniques are offline, e.g., data repair for large historical datasets on the Cloud.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [42]
Citations
Cites
Data-driven smart manufacturing
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.006 · Cited by 1,934
Time-series clustering – A decade review
Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015) · Information Systems · doi:10.1016/j.is.2015.04.007 · Cited by 1,826
Greenberg, A. G., Hamilton, J. R., Maltz, D. A., & Patel, P. (2008) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1496091.1496103 · Cited by 1,622
A review on time series data mining
Fu, T.-C. (2010) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2010.09.007 · Cited by 1,565
The industrial internet of things (IIoT): An analysis framework
Boyes, H. A., Hallaq, B., Cunningham, J., & Watson, T. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.04.015 · Cited by 1,496
Experimental comparison of representation methods and distance measures for time series data
Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., & Keogh, E. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0250-5 · Cited by 846
Smart manufacturing must embrace big data
Kusiak, A. (2017) · Nature · doi:10.1038/544023a · Cited by 636
Data mining in manufacturing: a review based on the kind of knowledge
Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2008) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-008-0145-x · Cited by 507
On-line outlier detection and data cleaning
Liu, H., Shah, S. L., & Jiang, W. (2004) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2004.01.009 · Cited by 491
Kim, W. B., Choi, B., Hong, E.-K., Kim, S., & Lee, D. (2003) · Data Mining and Knowledge Discovery · doi:10.1023/a:1021564703268 · Cited by 301
Research works printed but not linked: 2
- Cutler, 2014, The internet of manufacturing things, Ind. Eng., 46, 37
- Hinkle, 2003, vol. 663
Cited by
Sorted by citations
A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
Goknil, A., Nguyen, P., Sen, S., Politaki, D., Niavis, H., Pedersen, K. J., Suyuthi, A., Anand, A., & Ziegenbein, A. (2023) · ACM Computing Surveys · doi:10.1145/3593043 · Cited by 84
Metadata
- Authors
- Villalobos, K., Ramírez-Durán, V. J., Diez, B., Blanco, J. M., Goñi, A., & Illarramendi, A.
- Affiliations
- University of the Basque Country (ES)
- Corresponding author
- Villalobos, K.
- Venue
- Computers in Industry
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 121
- Pages
- 103257
- ISSN
- 0166-3615 (ISSN-L)
- OpenAlex
- W3036544642
- Cited by
- 35
- References
- 39
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 6 · 2023: 8 · 2022: 5 · 2021: 6
- FWCI
- 2.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Industry (publishedVersion)
- Funders
- MEC (FEDER/TIN2016-78011-C4-2-R)
- Basque Government (IT1330-19, PRE_2018_2_0263)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- Industry 4.0, time-series data storage, solid-state drives, hard disk drives, time series databases, document-oriented databases, graph databases, industrial monitoring, production data
About these checks
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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