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.

scholarly journals Challenges for the DOE methodology related to the introduction of Industry 4.0

2020 ◽  
Vol 26 (4) ◽  
pp. 190-194
Author(s):  
Jacek Pietraszek ◽  
Norbert Radek ◽  
Andrii V. Goroshko
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Integration ◽  
Design Of Experiments ◽  
Industry 4.0 ◽  
Data Stream ◽  
Data Driven ◽  
Industrial Data ◽  
Existing Problems

AbstractThe introduction of solutions conventionally called Industry 4.0 to the industry resulted in the need to make many changes in the traditional procedures of industrial data analysis based on the DOE (Design of Experiments) methodology. The increase in the number of controlled and observed factors considered, the intensity of the data stream and the size of the analyzed datasets revealed the shortcomings of the existing procedures. Modifying procedures by adapting Big Data solutions and data-driven methods is becoming an increasingly pressing need. The article presents the current methods of DOE, considers the existing problems caused by the introduction of mass automation and data integration under Industry 4.0, and indicates the most promising areas in which to look for possible problem solutions.

scholarly journals Adoption of Big Data by Global Chemical Industries

2021 ◽  
Author(s):  
Ashiff Khan ◽  
A Seetharaman ◽  
Abhijit Dasgupta
Keyword(s):  
Big Data ◽  
Data Science ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Time Data ◽  
New Era ◽  
Potential Applications ◽  
Data Driven Approach ◽  
Process Plants ◽  
The Right

The new era of Big Data (BD) is influencing the chemical industries tremendously, providing several opportunities to reshape the way they operate and for shifting towards smart manufacturing. Given the availability of free software, and the large amount of real-time data generated and stored in process plants why many chemical industries are still not fully adopting BD? The industry is just starting to realize the importance of a large amount of data that they own to make the right decisions and to support their strategies. This article is exploring the importance of professional competencies and data science that influence BD in chemical industries for shifting towards smart manufacturing in a fast and reliable manner. This article utilizes a literature review and identifies potential applications in the chemical industry to shift from conventional methods towards a data-driven approach.

Exploratory data analysis for pre and post 24/7/365 attending radiologist coverage support in an emergency department: fundamentals of data science

2019 ◽  
Vol 27 (3) ◽  
pp. 233-251
Author(s):  
Sabeena Jalal ◽  
Marshall E Lloyd ◽  
Faisal Khosa ◽  
Grace I-Hsuan Hsu ◽  
Savvas Nicolaou
Keyword(s):  
Emergency Department ◽  
Data Analysis ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Exploratory Data

scholarly journals A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning

2021 ◽  
Vol 54 (5) ◽  
pp. 1-40
Author(s):  
Mohanad Abukmeil ◽  
Stefano Ferrari ◽  
Angelo Genovese ◽  
Vincenzo Piuri ◽  
Fabio Scotti
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Representation Learning ◽  
Data Representation ◽  
Generative Models ◽  
Data Exploration ◽  
Generative Learning ◽  
Learning Models ◽  
Exploratory Data

For more than a century, the methods for data representation and the exploration of the intrinsic structures of data have developed remarkably and consist of supervised and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high and the data can come in messy, incomplete, unlabeled, or corrupted forms. Consequently, discovering the hidden structure buried inside such data becomes highly challenging. From this perspective, exploratory data analysis plays a substantial role in learning the hidden structures that encompass the significant features of the data in an ordered manner by extracting patterns and testing hypotheses to identify anomalies. Unsupervised generative learning models are a class of machine learning models characterized by their potential to reduce the dimensionality, discover the exploratory factors, and learn representations without any predefined labels; moreover, such models can generate the data from the reduced factors’ domain. The beginner researchers can find in this survey the recent unsupervised generative learning models for the purpose of data exploration and learning representations; specifically, this article covers three families of methods based on their usage in the era of big data: blind source separation, manifold learning, and neural networks, from shallow to deep architectures.

scholarly journals Big Data Analytics: A Spotify Case Study

2021 ◽  
Vol 9 (10) ◽  
pp. 1823-1829
Author(s):  
Suraj Ingle
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Big Data Analytics ◽  
Exploratory Data ◽  
Insight Into

Abstract: By developing products that are in line with consumer needs, anticipating their profitability and manufacturing them, Big Data has opened up a lot of possibilities for building customer loyalty and commercial business by proactively engaging and comprehensively streamlining offers across all customer touch points. The use of big data to determine the best, most efficient ways to engage and interact with their customers will be discussed in this paper. An insight into how Spotify intends to provide music lovers additional ways to find their favourite songs, interact with artists, and improve Spotify recommendations has been provided. Keywords: Big Data, Data Analytics, Customer Satisfaction, Exploratory Data Analysis

Using Exploratory Data Analysis and Big Data Analytics for Detecting Anomalies in Cloud Computing

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Ibrahim Muzaferija ◽  
Zerina Mašetić ◽  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Anomaly Detection ◽  
Data Analytics ◽  
Large Scale ◽  
Exploratory Data Analysis ◽  
Big Data Analytics ◽  
Detection Methods ◽  
Exploratory Data

While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle following up with the amount of data generated in terms of efficient processing and anomaly detection, which is a necessary part of the management of modern applications. As the record of user behavior, weblogs surely become the research item related to anomaly detection. Many anomaly detection methods based on automated log analysis have been proposed. However, not in the context of big data applications where anomalous behavior needs to be detected in understanding phases prior to modeling a system for such use. Big Data Analytics often ignores anomalous point due to high volume of data. To address this problem, we propose a complemented methodology for Big Data Analytics – the Exploratory Data Analysis, which assists in gaining insight into data relationships without the classical hypothesis modeling. In that way, we can gain better understanding of the patterns and spot anomalies. Results show that Exploratory Data Analysis facilitates anomaly detection and the CRISP-DM Business Understanding phase, making it one of the key steps in the Data Understanding phase.

scholarly journals Reproducible geospatial data science: Exploratory data analysis using collaborative analysis environments

2018 ◽  
Vol 70 (5) ◽  
pp. 1844-1859
Author(s):  
Alber Sánchez ◽  
Lubia Vinhas ◽  
Gilberto Queiroz ◽  
Rolf Simoes ◽  
Vitor Gomes ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Geospatial Data ◽  
Exploratory Data ◽  
Collaborative Analysis
Sign in / Sign up

Export Citation Format

Share Document