Data Science Analytics and Applications

This book offers the proceedings of the Second International Data Science Conference (iDSC2019), organized by Salzburg University of Applied Sciences, Austria. The Conference brought together researchers, scientists, and business experts to discuss new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The papers gathered here include case studies of applied techniques, and theoretical papers that push the field into the future. The full-length scientific-track papers on Data Analytics are broadly grouped by category, including Complexity; NLP and Semantics; Modelling; and Comprehensibility. Included among real-world applications of data science are papers on Exploring insider trading using hypernetworksData-driven approach to detection of autism spectrum disorderAnonymization and sentiment analysis of Twitter posts Theoretical papers in the book cover such topics as Optimal Regression Tree Models Through Mixed Integer Programming; Chance Influence in Datasets with Large Number of Features; Adversarial Networks A Technology for Image Augmentation; and Optimal Regression Tree Models Through Mixed Integer Programming. Five shorter student-track papers are also published here, on topics such as State-of-the-art Deep Learning Methods to effect Neural Machine Translation from Natural Language into SQLA Smart Recommendation System to Simplify Projecting for a HMI/SCADA Platform Use of Adversarial Networks as a Technology for Image AugmentationUsing Supervised Learning to Predict the Reliability of a Welding Process The work collected in this volume of proceedings will provide researchers and practitioners with a detailed snapshot of current progress in the field of data science. Moreover, it willstimulate new study, research, and the development of new applications.

Aktuelle Data Science Themen Spezifische Data Science Fragestellungen In einem Werk konzentriert

Autorentext

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 IT management and business informatics. In addition, he has coordinated several national and international research projects.

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

Klappentext

Advances in technology and changes in the business and social environment have led to an increasing flood of data, fueling both the need and the desire to generate value from these assets. The emerging field of Data Science is poised to deliver theoretical and practical solutions to the pressing issues of data-driven applications.

The 2nd International Data Science Conference (iDSC2019 / http://www.idsc.at) organized by Salzburg University of Applied Sciences brings together researchers, scientists, and business experts to discuss new ways of embracing agile approaches within the various domains of data science, e.g. machine learning and AI, data mining, visualization and communication, as well as case studies and best practices from leading research institutions and businesses. The proceedings include all full papers presented in the scientific track and the short papers from the student 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 IT management and business informatics. In addition, he has coordinated several national and international research projects.

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

Inhalt

German Abstracts.- Full Papers Double Blind Reviewed.- Data Analytics | Complexity.- Data Analytics | NLP and Semantics.- Data Analytics | Modelling.- Data Analytics | Comprehensibility.- Short Papers

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