Digital Business and Intelligent Systems
Description
Baltic DB&IS 2026 proceedings on Information systems, Artificial intelligence, Machine learning, Data management systems, Human computer interaction.
This book constitutes the refereed proceedings of the 17th International Baltic Conference, DB&IS 2026, Tartu, Estonia, June 28-July 1, 2026.
The 20 revised full papers were carefully reviewed and selected from 52 submissions. The papers are centered around topics like Foundations, Methods, and Systems Engineering,Applications of Intelligent Systems,Human-Centric Intelligent Systems,Socio-Technical Systems,Data Science and Analytics in Education and Digitalization and Intelligent Systems for Organisations.More details
Content
.- Foundations, Methods, and Systems Engineering
.- Information Systems Engineering: A Journey Through Half a Century of History.
.- When AI Changes the Rules: Managing Resistance in IT Projects through a Maturity Framework.
.- Towards a Fractal-based System Architecture for LLM-based Multiagent Systems.
.- Applications of Intelligent Systems
.- Towards Fully Synthetic Schema-based Tabular Test Data Generationin Estonian E-Government Settings.
.- Phase-Based Safety Analysis of Automated Driving System Using Simulations.
.- A Dual-Stage Cascade Deep Learning Framework for Automated Road Defect Detection and Severity Assessment.
.- Human-Centric Intelligent Systems
.- Digital and Traditional Educational Games: Children's Engagement and Problem-Solving Strategies in Early Childhood Education.
.- A Modular System for VR-Based Upper-Limb Rehabilitation: Adaptive Visual Feedback and Structured Kinematic Data Pipeline.
.- A Predictive Anthropometric Data Infrastructure for 3D Body Modeling.
.- Socio-Technical Systems
.- Quality Characteristics of Cyber-Physical-Social Systems: A Systematic Literature Review.
.- Man-LLM Shared Graph Workspace: Spec-First NL-to-VIS via Serialisation.
.- Constraint-Aware Data Collection and Observational Analysis of Digital Labor Platforms.
.- Data Science and Analytics in Education
.- Ensuring Data Privacy and Utility in Synthetic Data Generation for Analysis of International Student Admissions Data.
.- Integrating the Data Science into the K-12 Education: A Spiral Curriculum Model.
.- Hybrid Vision-Language Extraction for Inclusive and Accessible Educational Content.
.- Quantifying Success: Establishing Benchmark Retention Rates in Undergraduate CS Programs.
.- Digitalization and Intelligent Systems for Organisations
.- Governing AI Agents in Organizations: Toward an Opportunity- and Risk-Based Framework.
.- From Process Model to Fractal Enterprise Model: Practical Generation and Lessons Learned.
.- Digital Twin-Driven Supply Chain Resilience with Predictive and Optimization Techniques.
.- Understanding Generative AI Adoption in Development Work: The Role of Developer Experience.