Data Science - Analytics and Applications (eBook, PDF)

Proceedings of the 3rd International Data Science Conference - iDSC2020
Redaktion: Haber, Peter; Plankensteiner, Kathrin; Mayr, Manfred; Lampoltshammer, Thomas

• Format: PDF

Produktbeschreibung

Data Science and Artificial Intelligence have become the driving force of many businesses and are an integral part of our daily lives. Small and important decisions or the control of technical processes are based on knowledge gained from collected data. Advice from data driven analytics is integrated into business decisions and is the base of many new business models.

Research in Data Science is progressing rapidly, more and more companies have successfully applied Data Science and Artificial Intelligence in countless scenarios. To account for the close interaction between science and application, the International Data Science Conference brings together researchers, scientists, engineers and entrepreneurs. The 3rd iDSC organized by FH Vorarlberg under the patronage of the Salzburg University of Applied Sciences has given an insight into state-of-the art research topics as well as best practice applications from industries and businesses. New approaches in the fields of machine learning, artificial intelligence, data mining and visualization as well as first-hand experience, best practice examples and novel applications from companies have been discussed.

The proceedings include all full papers presented in the scientific track and the short papers from the industry track.

Editors

Peter Haber is a Professor of Information and Communication Technology, in particular for analog and digital signal processing, and responsible coordinator for system theory and electrical engineering at Salzburg University of Applied Sciences. He is a researcher and project manager, leading and coordinating national and international projects in the field of IT and IT management, while also integrating data science solutions at businesses. Since 2009 he has been a member of the international advisory board for the IATED conferences.

Thomas Lampoltshammer is an Assistant Professor for ICT and Deputy Head of the Centre for E-Governance at the Department of E-Governance and Administration, Danube University Krems, Austria. His current research focus is on the domain of data governance, the effects of ICT application in a connected society, and the effects on a data-driven society. He has a substantial background in the design and implementation of expert and decision-making systems, data analytics, and semantic-based reasoning.

Manfred Mayr is the Academic Program Director for "Business Informatics and Digital Transformation" as well department head for IT-Management at Salzburg University of Applied Sciences. He is a lecturer at international conferences and the author of various publications in the field of business informatics and researches business applications of data science. The digitalisation of ERP applications in the industrial environment is a long-standing and important field of his research. In addition, he has coordinated several national and international research projects.

Kathrin Plankensteiner is the Head of "Data Analytics & Intelligence" at the research center Digital Factory Vorarlberg, FH Vorarlberg University of Applied Science. She studied technical mathematics and data analysis and holds a PhD in Applied Statistics from the University of Klagenfurt (Austria). Her field of research includes reliability testing and analyzing, lifetime modeling, regression analysis, computational statistics, multivariate data analysis, statistical inference, reasoning, & statistical machine learning.

The editors are the conference chairs of the International Data Science Conference.

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Produktdetails

• Verlag: Springer Fachmedien Wiesbaden
• Seitenzahl: 120
• Erscheinungstermin: 4. Januar 2021
• Deutsch
• ISBN-13: 9783658321826
• Artikelnr.: 60855752

Inhaltsangabe

Abstracts of Industry Contributions.- Non Peer-Reviewed Invited Papers.- German Abstracts of Peer-Reviewed Full Papers.- Peer-Reviewed Full Papers.- Data Science Frameworks.- Bayesian Analysis.- Optimizing Methods for Data Science.- Deep Learning Applications