Section Information Systems (IS)
The section IS studies subjects related to the design, realization, analysis and optimization of information systems.
The section IS comprises four chairs:
The research of the Analytics for Information Systems (AIS) chair concentrates on formalisms for modeling and methods to discover and analyze models. Fundamental to the research group at the Eindhoven University of Technology is the choice for Petri nets as the language to precisely describe process dynamics also in complex settings at a foundational level. The choice for this language is what distinguishes our research group from research groups in more industrial engineering oriented information systems groups.
The focus of the Artificial Intelligence (AI) research group is primarily on the AI fundamentals, techniques, and tools/frameworks for successful applications of AI, e.g., in data science, automated reasoning, decision support systems, recommender systems, and chemical informatics. We focus on the theoretical foundations, the limits of feasibility, and efficient and scalable realization of AI methodologies and techniques. These investigations are typically driven by real world application partners and challenges.
Data-intensive systems are crucial in modern computing, analytics, and data science. The Database (DB) group studies core engineering and foundational challenges in scalable and effective management of big data. Current DB group research focuses primarily on problems in the management of massive graphs such as social and biological networks. Expertise within the group is on query language design, query optimization and evaluation, data analytics, data integration, and personalization.
The Data Mining (DM) chair studies techniques and knowledge discovery approaches that are at the core of data science. The group is known for its contributions to the areas of predictive analytics, automation of machine learning and networked science, subgroup discovery and exceptional model mining, and similarity computations on complex data.