Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 304

The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.

Manufacturing process data analysis pipelines: a requirements analysis and survey

Open access
Ismail, Ahmed, Truong, Hong-Linh, Kastner, Wolfgang·2019·Journal of Big Data
DOI 10.1186/s40537-018-0162-3
Open this paper in CoChat — free account

Abstract

Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s40537-018-0162-3

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 47 references

Research works printed but not linked: 34

  1. Choudri A. The Agile enterprise. In: ReVelle J, editor. Manufacturing handbook of best practices: an innovation, productivity, and quality focus. New York: CRC Press; 2001. p. 3–23.
  2. Corrêa H. Agile manufacturing as the 21st century strategy for improving manufacturing competitiveness. In: Gunasekaran A, editor. Agile manufacturing: the 21st century competitive strategy. Oxford: Elsevier Science Ltd; 2001. p. 3–23. · doi:10.1016/b978-008043567-1/50001-2
  3. Jirkovsky V, Obitko M, et al. Big Data analysis for sensor time-series in automation. In: International conference on emerging technology and factory automation (ETFA). IEEE; 2014. p. 1–8. · doi:10.1109/etfa.2014.7005183
  4. Puig Ramírez J. Asset optimization and predictive maintenance in discrete manufacturing industry. MA thesis. Universitat Politècnica de Catalunya; 2010.
  5. Ismail A, Kastner W. Vertical integration in industrial enterprises and distributed middleware. Int J Internet Protocol Technol. 2016;9(2/3):79–89. https://doi.org/10.1504/IJIPT.2016.079547 . · doi:10.1504/ijipt.2016.079547
  6. Ismail A, Kastner W. Discovery in SOA-Governed Industrial Middleware with mDNS and DNS-SD. In: International conference on emerging technology and factory automation (ETFA). IEEE. 2016. · doi:10.1109/etfa.2016.7733529
  7. Ismail A. Service oriented manufacturing infrastructure. Dissertation. Vienna: TU Wien; 2018.
  8. Zerbst J, Schaefer M, et al. Zone principles as cyber security architecture element for smart grids. In: 2010 IEEE PES innovative smart grid technologies conference Europe (ISGT Europe). 2010. https://doi.org/10.1109/ISGTEUROPE.2010.5638900 . · doi:10.1109/isgteurope.2010.5638900
  9. Bowonder B. An analysis of the Bhopal accident. Project Appraisal. 1987;2(3):157–68. · doi:10.1080/02688867.1987.9726622
  10. Liptak BG, Venczel K, et al. Instrument and Automation Engineers’ Handbook. Process measurement and analysis. 5th ed. Boston: CRC Press; 2016.
  11. Raul AC. The privacy, data protection and cybersecurity law review. English. 2014. ISBN: 978-1-909830-28-8.
  12. Bernard R. Information lifecycle security risk assessment: a tool for closing security gaps. Comput Secur. 2007;26:26–30. · doi:10.1016/j.cose.2006.12.005
  13. Appt S, Fietz E, et al. Smart manufacturing. The legal and regulatory challenges. Pinsent: Pinsent Masons LLP; 2015.
  14. Didier P, Macias F, et al. Converged plantwide ethernet (CPwE) design and implementation guide. Milwaukee: Cisco Systems, San Jose, Californiaand Rockwell Automation; 2011.
  15. Sawant N, Shah H. Big Data application architecture Q & A: a problem-solution approach. Expert’s voice in big data. New York: Apress. 2013. ISBN: 978-1-4302-6292-3. · doi:10.1007/978-1-4302-6293-0_1
  16. Sauter T, Soucek S, et al. Vertical integration. In: Wilamowski B, Irwin J, editors. Industrial communication systems. 2nd ed. London: CRC Press; 2011. p. 1–12.
  17. Wilschut T, Adan I.J, et al. Big Data in daily manufacturing operations. In: IEEE simulation conference (WSC). 2014. p. 2364–75. · doi:10.1109/wsc.2014.7020080
  18. Staff NRC. Massive data sets: proceedings of a workshop. Washington: National Academies Press; 1900.
  19. Lee F, Smith S. Yield Analysis and Data Management Using Yield ManagerTM. In: IEEE/SEMI 1998 IEEE/SEMI Advanced semiconductor manufacturing conference and workshop (Cat. No.98CH36168). 1998. pp. 19–30. https://doi.org/10.1109/ASMC.1998.731377 . · doi:10.1109/asmc.1998.731377
  20. Wang C, Zhao C, et al. A framework for management of massive knowledge in cloud environment. In: International conference on BioMedical engineering and informatics (BMEI). 2014. p. 843–7. https://doi.org/10.1109/BMEI.2014.7002889 . · doi:10.1109/bmei.2014.7002889
  21. Qanbari S, Zadeh S, et al. CloudMan: a platform for portable cloud manufacturing services. In: International conference on Big Data (Big Data). IEEE; 2015. p. 1006–14. https://doi.org/10.1109/BigData.2014.7004334 . · doi:10.1109/bigdata.2014.7004334
  22. Moyne J, Samantaray J, et al. Big data emergence in semiconductor manufacturing advanced process control. In: Annual SEMI advanced semiconductor manufacturing conference (ASMC). IEEE. 2015. p. 130–5. https://doi.org/10.1109/ASMC.2015.7164483 . · doi:10.1109/asmc.2015.7164483
  23. Gerrikagoitia J, Unamuno G, et al. Making sense of manufacturing data. In: International conference on informatics in control, automation and robotics, vol. 2. SciTePress. 2016. p. 590–4. · doi:10.5220/0005999005900594
  24. Chen C-C, Hung M-H, et al. Development of a cyber-physical-style continuous yield improvement system for manufacturing industry. In: International conference on automation science and engineering. IEEE. 2016. p. 1307–12. https://doi.org/10.1109/COASE.2016.7743559 . · doi:10.1109/coase.2016.7743559
  25. De Silva PP, De Silva PA. Ipanera: an industry 4.0 based architecture for distributed soil-less food production systems. In: Manufacturing & industrial engineering symposium (MIES). IEEE. 2016. p. 1–5. · doi:10.1109/mies.2016.7780266
  26. Huber M, Voigt M, et al. Big data architecture for the semantic analysis of complex events in manufacturing. In: Lecture Notes in Informatics (LNI), Proceedings—series of the Gesellschaft fur Informatik (GI). p. 353–60.
  27. Shi Y, Chen Y, et al. A data services-based quality analysis system for the life cycle of tire production”. In: Lecture notes in computer science 9936 2016. p. 715–29. https://doi.org/10.1007/978-3-319-46295-0_51 . · doi:10.1007/978-3-319-46295-0_51
  28. Fan X, Zhu X, et al. Big data analytics to improve photomask manufacturing productivity. In: International conference on industrial engineering and engineering management, Vol. 2017. IEEE, 2018. p. 2341–5. https://doi.org/10.1109/IEEM.2017.8290310 . · doi:10.1109/ieem.2017.8290310
  29. Jirkovský V, Obitko M. Enabling Semantics within Industry 4.0. In: Mařík V, Wahlster W, editors. Industrial applications of holonic and multi-agent systems, vol. 10444. Cham: Springer; 2017. p. 39–52. https://doi.org/10.1007/978-3-319-64635-0_4 . · doi:10.1007/978-3-319-64635-0_4
  30. Li X, Tu Z, et al. Deep-level quality management based on big data analytics with case study. In: 2017 Chinese Automation Congress (CAC). 2017. p. 4921–6. · doi:10.1109/cac.2017.8243651
  31. Xu W, Liu Q, et al. Energy condition perception and Big Data analysis for industrial cloud robotics. In: Procedia CIRP 61 2017. p. 370–5. https://doi.org/10.1016/j.procir.2016.11.164 . · doi:10.1016/j.procir.2016.11.164
  32. Noh K-S. Model of knowledge-based process management system using big data in the wireless communication environment. Wireless Personal Commun. 2018;98:3147–62. https://doi.org/10.1007/s11277-017-4769-z . · doi:10.1007/s11277-017-4769-z
  33. Arantes M, Bonnard R, et al. General architecture for data analysis in industry 4.0 using SysML and model based system engineering. In: International systems conference (SysCon). IEEE. 2018. p. 1–6. · doi:10.1109/syscon.2018.8369574
  34. Kirmse A, Kraus V, et al. An architecture for efficient integration and harmonization of heterogeneous, distributed data sources enabling big data analytics. In: International conference on enterprise information systems. INSTICC. SciTePress. 2018. p. 175–82. ISBN: 978-989-758-298-1. https://doi.org/10.5220/0006776701750182 . · doi:10.5220/0006776701750182

Cited by

See all 304

Metadata

Authors
Ismail, A., Truong, H.-L., & Kastner, W.
Venue
Journal of Big Data
Publisher
Springer Science and Business Media LLC
Published
2019-01-07
Volume
6
Issue
1
ISSN
2196-1115 (ISSN-L)
DOI
10.1186/s40537-018-0162-3
OpenAlex
W2909445826
Cited by
304
References
47
FWCI
25.46
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0 (tdm)
Funders
H2020 Marie Skłodowska-Curie Actions (764785)

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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Manufacturing process data analysis pipelines: a requirements analysis and survey. https://app.cochat.ai/papers/10.1186/s40537-018-0162-3
The paper
Ismail, A., Truong, H.-L., & Kastner, W. (2019). Manufacturing process data analysis pipelines: a requirements analysis and survey. Journal of Big Data. https://doi.org/10.1186/s40537-018-0162-3

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.