This paper attempts to answer a central question in unsupervised learning: what does it
mean to “make sense” of a sensory sequence? In our formalization, making sense involves …

To prevent problems and capitalise on opportunities before they even occur, the research
project SPEEDD proposed a methodology, and developed a prototype for proactive event …

Composite event recognition (CER) systems process streams of sensor data and infer
composite events of interest by means of pattern matching. Data uncertainty is frequent in …

The concept of event processing is established as a generic computational paradigm in
various application fields, ranging from data processing in Web environments, over maritime …

Complex Event Recognition (CER) systems detect event occurrences in streaming time-
stamped input using predefined event patterns. Logic-based approaches are of special …

Online structure learning approaches, such as those stemming from statistical relational
learning, enable the discovery of complex relations in noisy data streams. However, these …

Imagine a machine, equipped with sensors, receiving a stream of sensory information. It
must, somehow, make sense of this stream of sensory data. But what, exactly, does this …

Abstract Systems for symbolic complex event recognition detect occurrences of events in
time using a set of event definitions in the form of logical rules. The Event Calculus is a …

The Apperception Engine is an unsupervised learning system. Given a sequence of sensory
inputs, it constructs a symbolic causal theory that both explains the sensory sequence and …

Abstract Complex Event Recognition (CER) systems detect event occurrences in streaming
time-stamped input using predefined event patterns. Logic-based approaches are of special …