=Paper=
{{Paper
|id=Vol-500/paper-9
|storemode=property
|title=Knowledge-based Complex Event Processing
|pdfUrl=https://ceur-ws.org/Vol-500/paper9.pdf
|volume=Vol-500
}}
==Knowledge-based Complex Event Processing==
<pdf width="1500px">https://ceur-ws.org/Vol-500/paper9.pdf</pdf>
<pre>
          Knowledge-Based Complex Event Processing
                             Kia Teymourian
                         Email: kia@inf.fu-berlin.de
                   Freie Universität Berlin, August 2009

    Events play an important role in many different areas of computer sys-
tems, from small embedded applications to large heterogeneous distributed
systems. Events are real-world occurrences that are happening over space
and time. In computer science an event can be defined as any transient
occurrence of a happening of interest that can be observed from within a
computer system. In many of current computer systems, permanent stream
of huge amount of events need an intelligent real-time event processor. Event
processing means computing that performs operations on events, including
reading, creating, transforming, and deleting events.
    The capability of complex event detection and processing is one of the
critical success factors of event-driven systems. Complex events can be com-
posed or derived (synthesized) from raw simple events based on their in-
coming sequence, their syntax and semantics. The existing complex event
processing engines process events as plain data without much knowledge
(metadata) about them. Semantic (meta) models of events can improve the
quality of event processing by using event metadata in combination with
ontologies and rules (knowledge bases). An event instance is a concrete
semantic object containing data describing the event.
    Using semantics of events is one of the promising approaches for detec-
tion of real-world complex events. Knowledge about event types and their
hierarchies i.e. specialization, generalization, or other forms of relations
between events can be useful. For example one might define that in a su-
permarket, theft alarm is the same as fire alarm. Knowledge representation
goes beyond event types and their hierarchies, more interesting are the rela-
tionships of events to other non-event concepts which specify more complex
events and reactions. For example, one might define that in the case of
a theft alarm only staff members who had special security courses, should
be informed about the alarm. In this example we have non-event concepts
such as person, education, course. Our research aims to develop a knowledge
representation methodology for complex event processing (CEP) which in-
tegrates the domain and application specific ontologies for events, processes,
states, actions, and other concepts that relate to change over time. Specific
domain, task and application ontologies need to be dynamically connected
and integrated into the respective event processing applications, which also
leads to a modular integration approach for these ontologies. Capturing
domain-specific complex events and generating complex reactions based on
them is a fundamental challenge which we address in this Ph.D thesis.

</pre>