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Projekt Druckansicht

Prozessorientierung in Ereignisgetriebenen Systemen: Model, Analyse und Optimierung

Fachliche Zuordnung Sicherheit und Verlässlichkeit, Betriebs-, Kommunikations- und verteilte Systeme
Softwaretechnik und Programmiersprachen
Förderung Förderung von 2014 bis 2022
Projektkennung Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 246594964
 
Erstellungsjahr 2021

Zusammenfassung der Projektergebnisse

In domains such as healthcare, logistics, or commerce, information systems support processes, loosely defined as the coordinated execution of a set of actions to achieve a certain goal. Traditionally, such systems leverage process models to orchestrate the execution of the actions of a process. While such an approach works well for relatively static and closed application scenarios, it is limited in its support for processes that shall be guided dynamically, in response to data that is sensed from diverse sources. In such scenarios, techniques for event stream processing (ESP) may help to achieve effective support of processes. By evaluating queries over streams of event data, ESP enables continuous monitoring of the progress of process execution and, based thereon, provides means for reactive control. Using ESP in process-oriented applications, however, raises several questions concerning the relation between essential concepts of processes and event streams, as well as linked to models that may enable the analysis and optimisation of the respective ESP systems. The project “Process-Awareness of Event-Driven Systems: Model, Analysis, and Optimisation”, PAEDS for short, set out to investigate these research questions. The main scientific results of the project are summarized as follows: In PAEDS, we phrased the abstraction of low-level events needed to identify the execution of actions of a process as the problem of discovering event queries from labelled data. Moreover, we developed the IL-Miner, an algorithm to address this problem. ◦ We presented interval-based models, coined temporal network representations, for event data of processes, which naturally capture the durations of actions along with their interplay. ◦ Formalising abstractions of event data as fragments of process models, we developed algorithms for the discovery of frequent fragments of process behaviour. ◦ To obtain models for quantitative analysis from low-level event data, we developed algorithms for property-preserving generalization of performance models. ◦ To achieve best-effort event stream processing in overload situations, we developed state-based load shedding strategies that exploit the regularities in event streams produced by processes. ◦ Targetting the efficient integration of data from remote sources in event stream processing, we devised algorithms for data prefetching and caching based on stream characteristics. The above results lay the foundations for using event stream processing in process-oriented applications. The feasibility of our techniques has been demonstrated in numerous experimental evaluations. They highlight the specific benefits in terms of enabling novel types of insights or improving the effectiveness and efficiency of existing analysis techniques. At a more abstract level, we draw two main conclusions from our experience in the PAEDS project. First, a main challenge when working at the intersection of process-oriented systems and event stream processing is the generalizability of research results. Despite well-defined notions and base concepts for processes and event streams, there is a wide variety of specific realizations of these notions in practice and, hence, also of their integration and interplay. As such, it is of utmost importance to clearly spell out the assumptions imposed by models, analysis techniques, optimization strategies on the relation between processes and event streams. Second, the potential for impactful research on process-awareness in event stream processing turned out to be even larger than expected. In all three areas explored in PAEDS, i.e., integrated modelling, quantitative analysis, and optimisation strategies, various novel research questions and ideas emerged. They provide a valuable starting point for follow-up work, which, for instance, more focus in more detail on distributed infrastructure, privacy considerations, and data quality issues.

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