Inductive Logic Programming (ILP) is a subfield of machine learning, focusing on learning logical representations from relational data. The ILP conference series, started in 1991, is the premier international forum for learning from structured or semi-structured relational data, multi-relational learning and data mining. Originally focusing on the induction of logic programs, over the years it has expanded its research horizon significantly and welcomes contributions to all aspects of learning in logic, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches.
We will present the formal methods for Complex Event Recognition (CER), i.e. models based on automata and computational logic, as they have been developed in the database, distributed systems, and artificial intelligence communities. For each of these models, we will present the reasoning algorithms that support on-line event recognition, as well as event forecasting, i.e. the computation of future intervals in which an event is likely to happen. To illustrate the reviewed approaches we will use a real-world use case from the INFORE project: complex event recognition for maritime situational awareness.
Over the past decade, the ACM International Conference on Distributed and Event‐based Systems (DEBS) has become the premier venue for cutting-edge research in the field of event processing and distributed computing, and the integration of distributed and event-based systems in relevant domains such as Big Data, AI/ML, IoT, and Blockchain. The objectives of the ACM DEBS are to provide a forum dedicated to the dissemination of original research, the discussion of practical insights, and the reporting of experiences relevant to distributed systems and event‐based computing. The conference aims at providing a forum for academia and industry to exchange ideas through its tutorials, research papers, and the Grand Challenge.
In order to obtain timely insights and implement reactive and proactive measures, many contemporary applications require reasoning about actions and events over streams of continuously arriving data. For example, in a wide range of applications, critical activities are formalised as events that have to be detected in real-time, or even forecast ahead of time. The workshop aims to bring together researchers working in a variety of areas, such as knowledge representation, machine learning, database systems, complexity theory, distributed systems and business process modeling, and thus foster community building on reasoning on actions and events over streams.
The objective of this Dagstuhl Seminar is to: