Visual Analytics for Process Monitoring : From Time Series to Interpretable Visual Representations
Language: English
Published by Springer, 2026
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - In the era of big data, process industries face the challenge of analyzing massive and complex data to extract information for effective process monitoring. This book introduces a novel paradigm called visual analytics. Visual analytics transforms chronological process data into visual formats to uncover patterns. This paradigm allows process experts to relate visual patterns to operational conditions and, consequently, support informed decision-making. The book explores three pathways within the visual analytics paradigm: (i) feature engineering, in which predefined mappings are used to convert time series data into visual representations; (ii) architecture engineering, which develops neural network architectures to directly learn visual representations; and (iii) data engineering, which employs contrastive learning to highlight differences and similarities in the data without relying on annotations.
Seller Inventory # 9783032221254
- Title
- Visual Analytics for Process Monitoring : From Time Series to Interpretable Visual Representations
- Author
- R. Bhushan Gopaluni
- Publisher
- Springer
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3032221250
- ISBN 13
- 9783032221254
- Item weight
- 344 grams
- Dimensions
- 246x173x11 mm
In the era of big data, process industries face the challenge of analyzing massive and complex data to extract information for effective process monitoring. This book introduces a novel paradigm called visual analytics. Visual analytics transforms chronological process data into visual formats to uncover patterns. This paradigm allows process experts to relate visual patterns to operational conditions and, consequently, support informed decision-making.
The book explores three pathways within the visual analytics paradigm: (i) feature engineering, in which predefined mappings are used to convert time series data into visual representations; (ii) architecture engineering, which develops neural network architectures to directly learn visual representations; and (iii) data engineering, which employs contrastive learning to highlight differences and similarities in the data without relying on annotations.
"Synopsis" may belong to another edition of this title.
About the Author
Ibrahim Yousef received his PhD in Chemical and Biological Engineering from the University of British Columbia, Canada. His research focuses on industrial process monitoring and fault detection, with an emphasis on data-driven and visual analytics approaches for time-series analysis. He holds a bachelor’s degree in chemical engineering from the University of Abu Dhabi. His academic and research interests lie at the intersection of process systems engineering, data analytics, and machine learning for industrial applications.
Dr. Sirish L. Shah, PhD, FCAE, FCIC, FIEEE is Emeritus Professor with the Department of Chemical and Materials Engineering at the University of Alberta, where he held the NSERC-Matrikon-Suncor-iCORE Senior Industrial Research Chair in Computer Process Control from 2000 to 2012. He was on faculty at the University of Alberta from 1978 to 2016. Shah has held visiting appointments at Oxford University and Balliol College as a SERC fellow, Kumamoto University (Japan) as a Senior Research Fellow of the Japan Society for the Promotion of Science, among other appointments.
The main areas of Shah’s current research are process and performance monitoring, system identification and design, analysis and rationalization of alarm systems. Results from Shah’s research group have been translated into commercial software for process and performance monitoring and advanced alarm tools. He has consulted widely with the process industry and control software vendors.
Dr. R. Bhushan Gopaluni leads research activities at the UBC DAIS Lab. He is a Professor in the Department of Chemical and Biological Engineering and Vice-Provost and Associate Vice-President, Faculty Planning in the Office of the Provost and Vice-President Academic, UBC Vancouver. From 2017 to 2022, he was the Associate Dean for Education and Professional Development in the UBC Faculty of Applied Science. He received a Ph.D. from the University of Alberta in 2003 and a Bachelor of Technology from the Indian Institute of Technology, Madras in 1997 both in the field of chemical engineering.
He is one of the leading experts on data analytics for the processing industry and has authored over 110 refereed articles in reputed international Journals and conferences. His publications have been recognized through best paper awards and keynote presentations.
"About the title" may belong to another edition of this title.
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