Description
(Text)
Real-time data stream monitoring is crucial for process management in today's business environment. Using continuous data streams monitoring systems complex events can be detected that can trigger changes in control flow of business processes. This book presents a framework for knowledge-based event processing that integrates external background knowledge and improves expressiveness of event processing semantics. Fusion of available domain knowledge with streaming data can improve the event processing quality by enhancing the system to understand more about complex events and their relationships. A combinatorial event pattern specification is presented based on knowledge patterns and temporal event detection operators. The book explores three different approaches for real-time knowledge-based event processing: semantic enrichment of streams, enrichment of complex event patterns and type-based sampling of event streams.
(Author portrait)
Teymourian, Kia
Kia Teymourian is a postdoctoral researcher at Rice University. He received his doctorate degree from Free University of Berlin in 2014. His research interests include data stream processing, complex event processing, reactive rule-based systems, extended database technologies and large scale information systems in general.
