A Declarative Framework for Matching Iterative and Aggregative Patterns against Event Streams View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Darko Anicic , Sebastian Rudolph , Paul Fodor , Nenad Stojanovic

ABSTRACT

Complex Event Processing as well as pattern matching against streams have become important in many areas including financial services, mobile devices, sensor-based applications, click stream analysis, real-time processing in Web 2.0 and 3.0 applications and so forth. However, there is a number of issues to be considered in order to enable effective pattern matching in modern applications. A language for describing patterns needs to feature a well-defined semantics, it needs be rich enough to express important classes of complex patterns such as iterative and aggregative patterns, and the language execution model needs to be efficient since event processing is a real-time processing. In this paper, we present an event processing framework which includes an expressive language featuring a precise semantics and a corresponding execution model, expressive enough to represent iterative and aggregative patterns. Our approach is based on a logic, hence we analyse deductive capabilities of such an event processing framework. Finally, we provide an open source implementation and present experimental results of our running system. More... »

PAGES

138-153

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-22546-8_12

DOI

http://dx.doi.org/10.1007/978-3-642-22546-8_12

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051543138


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