A Methodology for Designing Events and Patterns in Fast Data Processing

Abstract

Complex Event Processing handles processing of a large number of heterogeneous events and pattern detection over multiple event streams in real-time. Situations of interests are modeled using event patterns which describe a specific situation in an event processing language. In order to leverage the usage of event processing in everyday situations, a clear methodology for the identification and definition of events and event patterns is needed. In this paper, we propose an end-to-end methodology for designing event processing systems. This methodology integrates domain knowledge modeled during the setup phase of event processing with a high-level event pattern language which allows users to create specific business-related patterns. In addition, our methodology regards the circumstance that some patterns might have to be defined by technical experts and therefore introduces an actor model. Our approach is validated based on a real use case of a supplier of convenience stores.

SEPP: Semantics-Based Management of Fast Data Streams

“…A more detailed discussion of this methodology can be found in our previous work [6]. In the following sections, we extend this with the concept of Semantic Event Processing Agents (SEPA) as a high-level abstraction of event processing logic.…”

StreamPipes

“…To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Our solution is based on StreamPipes [2,3], a framework that aims to improve modeling of stream processing pipelines. We consider a pipeline as a composition of distributed event processing agents (EPA), each of them performing a specific task.…”