Paper Info
Title
A declarative framework for matching iterative and aggregative patterns against event streams
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.
Year
DOI
Venue
2011
10.1007/978-3-642-22546-8_12
RuleML Europe
Keywords
Field
DocType
event processing framework,corresponding execution model,real-time processing,declarative framework,effective pattern,language execution model,expressive language,event processing,important class,complex pattern,aggregative pattern,event stream
Click stream analysis,Data mining,Programming language,Computer science,Event stream,Complex event processing,Mobile device,Execution model,STREAMS,Pattern matching,Database,Semantics
Conference
Volume
ISSN
Citations 
6826
0302-9743
1
PageRank 
References 
Authors
0.36
11
4
Name
Order
Citations
PageRank
Darko Anicic143731.28
Sebastian Rudolph296659.18
Paul Fodor320812.58
Nenad Stojanovic41605152.52