Evidence map›Paper›PMID 36560226›Full record

ArticleSensors (Basel, Switzerland)2022

Data Integration from Heterogeneous Control Levels for the Purposes of Analysis within Industry 4.0 Concept.

Tibor Horak, Peter Strelec, Michal Kebisek, Pavol Tanuska, Andrea Vaclavova

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tibor HorakInstitute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 91724 Trnava, Slovakia.ORCID 0000-0002-1620-6199
Peter StrelecInstitute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 91724 Trnava, Slovakia.ORCID 0000-0002-2732-4799
Michal KebisekInstitute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 91724 Trnava, Slovakia.ORCID 0000-0002-3771-3835
Pavol TanuskaInstitute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 91724 Trnava, Slovakia.ORCID 0000-0001-7025-1911
Andrea VaclavovaInstitute of Applied Informatics, Automation and Mechatronics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 91724 Trnava, Slovakia.ORCID 0000-0001-6498-1969

Funding

Proactive control of hybrid production systems using simulation-based digital twin VEGA 1/0176/22Proposal of identification and monitoring of production equipment parameters for the need for predictive maintenance in accordance with the concept of Industry 4.0 using Industrial IoT technologies. VEGA 1/0193/22
6 · The paper itself

Abstract

Small- and medium-sized manufacturing companies must adapt their production processes more quickly. The speed with which enterprises can apply a change in the context of data integration and historicization affects their business. This article presents the possibilities of implementing the integration of control processes using modern technologies that will enable the adaptation of production lines. Integration using an object-oriented approach is suitable for complex tasks. Another approach is data integration using the entity referred to as tagging (TAG). Tagging is essential to apply for fast adaptation and modification of the production process. The advantage is identification, easier modification, and generation of data structures where basic entities include attributes, topics, personalization, locale, and APIs. This research proposes a model for integrating manufacturing enterprise data from heterogeneous levels of management. As a result, the model and the design procedure for data integrating production lines can efficiently adapt production changes.

Indexed as

CommerceIndustrybig datadata analysisintegrationmanufacturing execution systemproduction line

Identifiers

PMID36560226
PMCPMC9786177

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.