With advancements in technology over the last ten years, data management 
issues have evolved from a stored persistent form to also include streaming 
data generated from sensors and other software monitoring tools. 
Furthermore, distributed, event-based systems are becoming more prevalent,
with a need to develop applications that can dynamically respond to 
information extracted from data streams. This research is investigating the
integration of stream processing and event processing techniques, with
expressive filtering capabilities that include queries over persistent databases
to provide application context to the filtering process. Distributed Event 
Processing Agents (DEPAs) continuously filter events from multiple data 
streams of different formats that provide XML views. Composite events for 
data streams are expressed using CXQ, a language that extends XQuery with
temporal, composite event language features, including operators for
expressing sequence, disjunction, conjunction, repetition, aggregation, and
time windows for events. Continuous queries and composite event filters are 
integrated with techniques for materialized view maintenance and 
incremental evaluation in condition monitoring to provide efficient ways of 
enhancing stream filters with database queries. The filtering and event 
detection load is distributed among multiple DEPAs, with CXQ expressions 
decomposed to allocate subcomponents of the expression to DEPAs that 
efficiently communicate in the global detection of  composite events. A unique 
aspect of our research is that it extends XQuery with temporal, composite 
event  features to combine techniques for continuous queries in stream 
processing, incremental evaluation in condition monitoring, and detection and 
filtering of composite events, creating an expressive environment for the 
extraction of meaningful events from multiple data streams with XML views. 
 

Our approach to the integration of stream processing and event processing is
essential to the support of applications such as those in the medical field for 
health monitoring, the financial domain for executive dashboards, supply 
chain and other B2B applications for monitoring consumer activity, or for 
autonomic behavior within computer systems and embedded systems. With 
XML becoming a standard for data representation, our research will provide 
extensions to XQuery that will provide a uniform framework of time-based 
composite event detection to streams of data and events in different formats. 
On a broader scale, our research will enhance the analysis of data and event 
streams through the use of database context filters and through the 
correlation of multiple streams, thus providing a way of extracting more 
meaningful, application-oriented events from streams of data in the support 
of dynamic, data-driven applications.