PhD-defence Jan Carlson

Title: Event Pattern Detection for Embedded Systems

Events play an important role in many computer systems, from small
reactive embedded applications to large distributed systems. Many
applications react to events generated by a graphical user interface or
by external sensors that monitor the system environment, and other
systems use events for communication and synchronisation between
independent subsystems. In some applications, however, individual event
occurrences are not the main point of concern. Instead, the system
should respond to certain event patterns, such as "the start button
being pushed, followed by a temperature alarm within two seconds". One
way to specify such event patterns is by means of an event algebra with
operators for combining the simple events of a system into
specifications of complex patterns.

This thesis presents an event algebra with two important
characteristics. First, it complies with a number of algebraic laws,
which shows that the algebra operators behave as expected.  Second, any
pattern represented by an expression in this algebra can be efficiently
detected with bounded resources in terms of memory and time, which is
particularly important when event pattern detection is used in embedded
systems, where resource efficiency and predictability are crucial.

In addition to the formal algebra semantics and an efficient detection
algorithm, the thesis describes how event pattern detection can be used
in real-time systems without support from the underlying operating
system, and presents schedulability theory for such systems. It also
describes how the event algebra can be combined with a component model
for embedded system, to support high level design of systems that react
to event patterns.



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Last modified on May 2007 by Anne Moe