One of ever open issues in many organization systems and research centers is how to transform large amounts of daily collected data into useful knowledge from the perspective of declared goals and expected, tangible values. One of the central roles in addressing this issue is still played by modern information systems, providing a variety of intelligent services for that purpose. The main concerns of this track are about utilizing Data Science (DS), Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), Data Analytics (DA), and related paradigms, as a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information, knowledge, and value, and by this greatly support the information management process in business or research organizations. Thus, various interdisciplinary-oriented DS & ML approaches may provide organizations the ability to use their data to improve the quality of business, provide high-quality decision-making, increase financial efficiency and operational effectiveness, conduct innovative research, or satisfy regulatory requirements, in various business or research domains.

The main goal of this track is to address open questions and real potential for various applications of modern approaches in DS & ML so as to develop and implement effective software services in support of information management in various organization systems. Nowadays, many information systems are intended to provide various DS & ML services to describe, analyze, cluster, classify, evaluate, predict, and visualize. We refer here to the approaches that deploy DS & ML both over strongly and weakly structured data (also including textual, image, time-series, or multimedia data), and by this, we do not exclude computer vision or natural language processing approaches.

On the other hand, information system development/management is increasingly benefiting from advances in DS & ML. There is a wide room for providing robust DS & ML approaches in software engineering and information system development, in order to analyze and evaluate complex software, software processes, or software configurations. Various DS & ML approaches with repository mining can enable targeted insights and powerful predictions for software quality, software development, and software project management, to provide. Nowadays, such approaches are inevitable in large software companies to come to a successful software process that is more reliable, more effective, faster, and of a lower cost.

Track topics include (but are not strictly limited to):

DS & ML – Theoretical and Practical Aspects
DS & ML – Applications in Various Problem and Research Domains
DS & ML for Business Analytics and Decision Making
DS & ML for Customer Support and Service Management
DS & ML for Quantitative Finances and Operations
DS & ML for Quality Management and Standardization
DS & ML for Business Process Automation
DS & ML for Project Management
DS & ML for Software Development and Testing
DS & ML for IT Operations and for Managing IT Infrastructures
DS & ML for Fraud Detection
DS & ML for Information Systems
DS & ML for Health and Bioinformatics

Track Chairs
Ivan Luković, University of Belgrade, Faculty of Organizational Sciences, Serbia
Boris Delibašić, University of Belgrade, Faculty of Organizational Sciences, Serbia