Abstract
The Internet of Things (IoT) enables continuous monitoring of phenomena based on sensing devices as well as analytics opportunities in smart environments. Complex Event Processing (CEP) comprises a set of techniques for making sense of the behavior of a monitored system by deriving higher level knowledge from lower level system events. Business Process Management (BPM) attempts to model processes and ensures that executed processes con-form with a predefined sequence. In IoT scenarios frequently a large number of events has to be analyzed in real-time to allow an instant response. While BPM reaches its limits in such situ-ations, CEP is able to analyze and process high volume streams of data in real-time. The evaluation and execution of rules and models of both paradigms are currently based on separate formalisms and are frequently implemented in heterogeneous systems. The presented paper integrates both domains by proposing an execution approach for multi-perspective declarative process process models completely based on CEP. The efficiency of the combined paradigms is validated in an implemented demonstration with simulated and real-life sensor data.
Highlights
Background and Related Workwe describe event-driven systems, basics of multi-perspective declarative process modelling and give an overview of related approaches.1.1 Event-Driven SystemsProcesses in our everyday life and business related procedures are influenced and triggered by various events
While Business Process Management (BPM) reaches its limits in such situations, Complex Event Processing (CEP) is able to analyze and process high volume streams of data in realtime
Jergler et al [18] propose a version of the Guard-Stage-Milestone model (GSM) based on CEP to specify life cycle processes of business artifacts
Summary
Processes in our everyday life and business related procedures are influenced and triggered by various events. The three basic steps of event-driven systems are: (i) Sense: The starting point is the recognition of relevant information or facts by sources like sensors. This information is interpreted as events and reflects a relevant part of the state of reality. As soon as data from event sources like sensors, network data, or news tickers arrive, they are processed by a CEP engine using predefined rules to detect patterns and derive complex events. This process can be repeated on several levels of abstraction. This way, processes are monitored but automatic actions can be triggered [5]
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