EPAComp: An Architectural Model for EPA Composition

Abstract

Context: Complex Event Processing (CEP) architectures present high applicability in Realtime Streaming Analytics (RTSA) by extracting and generating valuable information from continuous data feeds, like in stock markets, traffic, and patient monitoring. Problem: Although guidelines and models for CEP architectures have been proposed, the composition of its inter-operable elements in charge of processing events, known as Event Processing Agent (EPA), is challenging for software architects. Solution: This work proposes EPAComp, a model that covers this gap and addresses large-scale processing requirements through features such as stream-based constructions and specialized EPAs. IS Theory: We employed the Representation theory to create a model representing information systems for event processing. Method: The model was applied in a real case experiment to create a solution to collect streams of events from around 200 systems and to provide a dashboard for monitoring their usage. Besides, industry experts qualitatively evaluated the proposal. Results: The experiment results show an application of the model to handle heterogeneous data in a scalable and efficient manner according to indicators regarding performance, the assertiveness of processed output, degree of cohesion, and coupling of components. The qualitative results present that experts asserted EPAComp capabilities fit RTSA requirements. Contributions: An architectural model for EPA composition that enhances the literature by (i) representing static and dynamic EPA compositions through arrangements of specific aggregation structures; (ii) defining the state-of-the-art event processing strategies in CEP; and, (iii) organizing the hierarchy of EPA types.