  This paper shows that characterizing co-occurrence between events is an
important but non-trivial and neglected aspect of discovering potential causal
relationships in multimedia event streams. First an introduction to the notion
of event co-occurrence and its relation to co-occurrence pattern detection is
given. Then a finite state automaton extended with a time model and event
parameterization is introduced to convert high level co-occurrence pattern
definition to its corresponding pattern matching automaton. Finally a
processing algorithm is applied to count the occurrence frequency of a
collection of patterns with only one pass through input event streams. The
method proposed in this paper can be used for detecting co-occurrences between
both events of one event stream (Auto co-occurrence), and events from multiple
event streams (Cross co-occurrence). Some fundamental results concerning the
characterization of event co-occurrence are presented in form of a visual co-
occurrence matrix. Reusable causality rules can be extracted easily from
co-occurrence matrix and fed into various analysis tools, such as
recommendation systems and complex event processing systems for further
analysis.
