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 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

scholarly journals How R helps Airbnb make the most of its data

2017 ◽  
Author(s):  
Ricardo Bion ◽  
Robert Chang ◽  
Jason Goodman
Keyword(s):  
Data Analysis ◽  
Predictive Models ◽  
Data Science ◽  
Recent Survey ◽  
Specific Advice

At Airbnb, R has been amongst the most popular tools for doing data science in many different contexts, including generating product insights, interpreting experiments, and building predictive models. In a recent survey of the Airbnb team, 73% of Data Scientists and Analysts rated themselves as closer to “Expert” than “Beginner” in using R, and 58% regularly use R as a language for data analysis. Airbnb supports R usage by creating internal R tools and by creating a community of R users. At the end of the post, the authors provide some specific advice for practitioners who wish to incorporate R into their day-to-day workflow.

Low Frequency DAS Data Study with Integrated Data Analysis for Monitoring Hydraulic Fracture

2020 ◽  
Author(s):  
M. Ichikawa ◽  
M. Kato ◽  
S. Uchida ◽  
K. Tamura ◽  
A. Kato ◽  
...  
Keyword(s):  
Data Analysis ◽  
Hydraulic Fracture ◽  
Low Frequency ◽  
Integrated Data Analysis
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