MEdit4CEP-CPN: An approach for complex event processing modeling by prioritized colored petri nets
Complex Event Processing (CEP) is an event-based technology that allows us to process and correlate large data streams in order to promptly detect meaningful events or situations and respond to them appropriately. CEP implementations rely on the so-called Event Processing Languages (EPLs), which are...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/20312 |
| Acceso en línea: | http://hdl.handle.net/10578/20312 |
| Access Level: | acceso abierto |
| Palabra clave: | Formal modeling Petri nets Event-based system Complex event processing Event processing language Model-driven engineering Business process management |
| Sumario: | Complex Event Processing (CEP) is an event-based technology that allows us to process and correlate large data streams in order to promptly detect meaningful events or situations and respond to them appropriately. CEP implementations rely on the so-called Event Processing Languages (EPLs), which are used to implement the specific event types and event patterns to be detected for a particular application domain. To spare domain experts this implementation, the MEdit4CEP approach provides them with a graphical modeling editor for CEP domain, event pattern and action definition. From these graphical models, the editor automatically generates a corresponding Esper EPL code. Nevertheless, the generated code is syntactically but not semantically validated. To address this problem, MEdit4CEP is extended in this paper by Prioritized Colored Petri Net (PCPN) formalism, resulting in the MEdit4CEP-CPN approach. This approach provides both a novel PCPN domain-specific modeling language and a graphical editor. By using model transformations, event pattern models can be automatically transformed into PCPN models, and then into the corresponding PCPN code executable by CPN Tools. In addition, by using PCPNs we can compare the expected output with the actual output and can even conduct a quantitative analysis of the scenarios of interest. To illustrate our approach, we have conducted an air quality level detection case study and we show how this novel approach facilitates the modeling, simulation, analysis and semantic validation of complex event-based systems. |
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