Event processing (EP) is a data processing technology that conducts online processing of
event information. In this survey, we summarize the latest cutting-edge work done on EP …

Complex event recognition (CER) applications exhibit various types of uncertainty, ranging
from incomplete and erroneous data streams to imperfect complex event patterns. We …

A multitude of different probabilistic programming languages exists today, all extending a
traditional programming language with primitives to support modeling of complex, structured …

Urban traffic gathers increasing interest as cities become bigger, crowded and “smart”. We
present a system for heterogeneous stream processing and crowdsourcing supporting …

Detecting complex events from subsymbolic data streams (such as images, audio
recordings or videos) is a challenging problem, as traditional symbolic approaches cannot …

Symbolic event recognition systems have been successfully applied to a variety of
application domains, extracting useful information in the form of events, allowing experts or …

The field of probabilistic logic programming (PLP) focuses on integrating probabilistic
models into programming languages based on logic. Over the past 30 years, numerous …

This paper introduces a Surveillance Video Analysis System, called SVAS, for surveillance
domain, in which the semantic rules and the definition of event models can be learned or …

Norms provide a way to model the social architecture of a sociotechnical system (STS) and
are thus crucial for understanding how such a system supports secure collaboration …

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 …