Industrial Data Science in Wertschöpfungsnetzwerken

2019 ◽  
Vol 114 (12) ◽  
pp. 874-877 ◽  
Author(s):  
Jürgen Mazarov ◽  
Patrick Wolf ◽  
Julian Schallow ◽  
Fabian Nöhring ◽  
Jochen Deuse ◽  
...  
Keyword(s):  
Data Science ◽  
Industrial Data

Towards integrated Data Analysis Quality: Criteria for the application of Industrial Data Science

2021 ◽  
Author(s):  
Nikolai West ◽  
Jonas Gries ◽  
Carina Brockmeier ◽  
Jens C. Gobel ◽  
Jochen Deuse
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Quality Criteria ◽  
Industrial Data ◽  
Analysis Quality ◽  
Integrated Data Analysis

scholarly journals Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2772 ◽  
Author(s):  
Aguinaldo Bezerra ◽  
Ivanovitch Silva ◽  
Luiz Affonso Guedes ◽  
Diego Silva ◽  
Gustavo Leitão ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Exchange ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Data Driven ◽  
Industrial Data ◽  
Industrial Big Data ◽  
Data Driven Approach ◽  
Exploratory Data

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

Industrial Data Science erfolgreich implementieren

2020 ◽  
Vol 115 (10) ◽  
pp. 734-737
Author(s):  
Viktoria Nolte ◽  
Tanja Sindram ◽  
Jürgen Mazarov ◽  
Jochen Deuse
Keyword(s):  
Change Management ◽  
Data Science ◽  
Industrial Data

Abstract Die Potenziale von Industrial Data Science haben Unternehmen unlängst erkannt, scheitern jedoch an deren Umsetzung. In diesem Beitrag werden die Ergebnisse einer branchenübergreifenden Interviewstudie mit über 50 Führungskräften und Fachexperten vorgestellt, wobei Durchführungshemmnisse und Erfolgsfaktoren identifiziert werden. Zudem werden Anforderungen an das Change Management diskutiert sowie konkrete Handlungsempfehlungen für Unternehmen gegeben.

scholarly journals Towards the Automation of Industrial Data Science: A Meta-learning based Approach

2021 ◽  
Author(s):  
Moncef Garouani ◽  
Adeel Ahmad ◽  
Mourad Bouneffa ◽  
Arnaud Lewandowski ◽  
Gregory Bourguin ◽  
...  
Keyword(s):  
Data Science ◽  
Industrial Data ◽  
Meta Learning

Industrial Data Science erfolgreich implementieren

2020 ◽  
Vol 115 (10) ◽  
pp. 734-737
Author(s):  
Viktoria Nolte ◽  
Tanja Sindram ◽  
Jürgen Mazarov ◽  
Jochen Deuse
Keyword(s):  
Data Science ◽  
Industrial Data

Qualitätsprognose anhand Prozess-parametern einer Papiermaschine mittels Industrial Data Science

2019 ◽  
Vol 114 (5) ◽  
pp. 310-313
Author(s):  
Anton Stockert ◽  
Robert Glawar ◽  
Fazel Ansari ◽  
Wilfried Sihn
Keyword(s):  
Data Science ◽  
Industrial Data

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
Sign in / Sign up

Export Citation Format

Share Document