Stream Reasoning Tutorial

Stream reasoning at a glance

In many applications of the Internet of Things, manufacturing digitalization, or cyber-physical systems decisions must be made in a (near-) real-time over continuously changing data. This tutorial provides an in-depth introduction to stream reasoning formalisms with the focus on approaches originating in logic programming.

Increased availability of streaming data, such as sensor readings, results computed by software components, etc., are often coming at velocity and volumes which do not allow for their storage in databases, but are directly forwarded to its consumers. Stream reasoning is an emerging research area that aims at the development of knowledge representation and reasoning systems allowing for a (near-) real-time processing of continuously changing data. Formal languages of these systems are equipped with temporal modalities and different types of window operators that can be used to address snapshots of data from a stream. In conjunction with specifically designed methods for incremental and distributed computations, stream reasoning systems can be used to model and solve various complex problems such as diagnosis, configuration, or planning.

The goal of the tutorial is to introduce modern rule-based stream reasoning systems to a broad audience interested in efficient decision making over streaming data. The presentation provides an in-depth introduction of stream reasoning methods and includes all required preliminaries and therefore is suitable for a broad audience with a basic understanding of knowledge representation and reasoning techniques.

Tutorial program

First slot

  1. Motivation

  2. Stream Reasoning Overview

    1. Background

    2. Stream Processing

    3. Databases

    4. Complex Event Processing

    5. Temporal Reasoning

Second slot

  1. LP-based Formalisms

    1. Datalog for Stream Reasoning

    2. Prolog

    3. ASP-based Formalisms

    4. LARS

  2. Emerging Technologies

  3. Conclusion


Thomas Eiter

is a full professor at Vienna University of Technology. Among widely recognized activities in the field of AI, he has initiated the Stream Reasoning Workshop series, which after Vienna (2015), Berlin (2016), Zurich (2018) and Linköpping (2019), co-organized other related events e.g. at ISWC 2016, and co-edited a special issue of the Semantic Web Journal on Stream Reasoning.

Konstantin Schekotihin

is an associate professor at University Klagenfurt where he focuses on research of knowledge representation and reasoning techniques and their various applications including stream reasoning. He actively contributes to different events related to reasoning over streams and leads development of a distributed stream reasoner based on a popular Answer Set Programming paradigm.

Patrik Schneider

is currently pursuing his PhD at Vienna University of Technology. At the same time, he is currently working as a research assistant in the LocTraffLog project at Siemens CT, Austria, where lightweight methods of Knowledge Representation and Reasoning are developed for event detection and diagnosis in Cooperative Intelligent Transport Systems.