Abstract. Complex Event Processing -the identification of event patterns in event streams, the analysis of their impact, and the execution of corresponding actions -is gaining momentum within various research disciplines and business areas. However, one of the major problems associated with Complex Event Processing is its lack of usability, especially the complexity of its management, preventing its wide diffusion and adoption by users. This usability issue is addressed in this paper by applying Business Process Modeling Notation as graphical support for the definition of complex event patterns.
Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation.When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.
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