Based on the overall digitalization in all spheres of our lives, Data Science and Artificial Intelligence (AI) are nowadays cornerstones for innovation, problem solutions, and business transformation. Data, whether structured or unstructured, numerical, textual, or audiovisual, put in context with other data or analyzed and processed by smart algorithms, are the basis for intelligent concepts and practical solutions. These solutions address many application areas such as Industry 4.0, the Internet of Things (IoT), smart cities, smart energy generation, and distribution, and environmental management. Innovation dynamics and business opportunities for effective solutions for the essential societal, environmental, or health challenges, are enabled and driven by modern data science approaches.

The iDSC is designed as a conference with a dual approach: By bringing together the latest findings in research and science, as well as innovative implementation examples in business and industry, the conference is aimed at reflecting the current scientific breakthroughs and application expertise, as a means of stimulating shared professional discourse. The iDSC realizes this approach via its Research & Science track, the Application & Use Cases track, as well as interactive workshops. In addition, iDSC is striving to provide a PhD colloquium, enabling young researchers to exchange with senior faculty, industry experts, as well as other fellow students. All these activities are complemented with exciting keynotes and panels, providing international exchange and the discussion of current and next-gen trends in the domain of data science.

Addresses the latest topics in Data Science and Artificial Intelligence Collects papers from the 6th Interdisciplinary Data Science Conference - iDSC 2025 Deals with a wide range of topics, from Industry 4.0, IoT to Smart Cities and Low Code

Autorentext

Peter Haber is Assistant Professor and Head of Laboratory in the Department of Information Technologies and Digitalisation at Salzburg University of Applied Sciences, coordinating the System Theory & Mechatronics unit. As a Senior Researcher and Project Manager, he has led and contributed to numerous national and international research projects in IT and IT management. His work focuses on applying Data Science solutions in business and industrial contexts. He is also co-author of various scientific publications in the fields of simulation, e-learning, and project-based education. Since 2009, he has been a member of the International Advisory Board of the IATED conferences, actively contributing to global academic exchange and innovation.

Thomas Lampoltshammer is an Associate Professor for ICT and Deputy Head of the Centre for E-Governance at the Department of E-Governance and Administration, University for Continuing Education Krems, Austria. His current research focuses 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 designing and implementing expert and decision-making systems, data analytics, and semantic-based reasoning.

Manfred Mayr is responsible for "Management & Communication" within the "Information Technologies and Digitalisation" department and is a professor of IT Management & Digital Leadership at Salzburg University of Applied Sciences. He is a lecturer at international conferences and the author of numerous publications in the field of business informatics and its applications. The digitalisation of ERP applications in industrial environments has been a long-standing and important research focus of his. He has also been responsible for several national and international research projects as project manager.

Inhalt

One-Class Domain Adaptation via Meta-Learning.- Optimal Sizing and Operation of a Battery Energy Storage System for Demand Response in a Food Processing. Plant using Deep Learning for Load Forecasting.- Using reinforcement learning to optimize frequency control of autonomous parking garages.- Achieving Technical Interoperability for Privacy-Enhancing Technologies in Data Spaces.- Embedding the MLOps Lifecycle into OT Reference Models.- Towards Connected Digital Product Carbon Footprint Passports.- Do we need Complex Deep Learning Models for Inferring Body Weight from CT Scans? Initial Insights from an Exploratory Study.- Inuence of Body Composition on the Validity and Reliability of a Wearable Stride and Respiration Sensor.- Persistence-based Hough Transform for Line Detection.- Stiemer's Train Itineraries Enable Mobility Evaluating Research.- Topology-driven identication of repetitions in multi-variate time series.-Context-Aware Content Moderation for German Newspaper Comments.- Evaluating the Eectiveness of Large Language Models in Prompt-based Time Series Imputation.- GPU-Jupyter: A Framework for Reproducible Deep Learning Research.- AI4SimProd - AI-Assisted Simulation and Digital Twining for Ecient Industrial Production.- Machine Learning Assisted Optimization of 5G Network Parametrization.- Predictive Maintenance for Wind Turbines: Leveraging Sensor Data with Traditional and Novel Machine Learning Techniques.- The Self-Driving Company: An overview of the model.