The concept of event processing is established as a generic computational paradigm in various application fields, ranging from data processing in Web environments, over logistics and networking, to finance and medicine [Cugola and Margara 2012]. Events report on state changes of a system and its environment. Event recognition (event pattern matching [Luckham 2002]), in turn, refers to the detection of events that are considered relevant for processing, thereby providing the opportunity to implement reactive measures. Examples consist of the recognition of attacks in computer network nodes [Dousson and Maigat 2007], human activities on video content [Brendel et al. 2011], emerging stories and trends on the Social Web1, traffic and transport incidents in smart cities [Artikis et al. 2014b], fraud in electronic marketplaces [Schultz-Møller et al. 2009], cardiac arrhythmias [Callens et al. 2008], and epidemic spread [Chaudet 2006]. In each scenario, event recognition allows one to make sense of large data streams and react accordingly. Event recognition systems become increasingly important as we move from an information economy to an “intelligent economy,” where it is not only the accessibility to information that matters but also the ability to analyse and act upon information, creating a competitive advantage in commercial transactions, enabling the sustainable management of communities, and promoting the appropriate distribution of social, healthcare, and educational services [Vesset et al. 2011]. Current businesses tend to be unable to make sense of the amounts of data that are generated by the increasing number of distributed data sources that are available daily [Manyika et al. 2011], and they need to rely more and more on automated event recognition. As an example, consider traffic management in smart cities that needs to make use of data from an increasing number and variety of sensors.