Contributions

The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. Applications that generate rapid, continuous, and large volumes of event streams include readings from sensors used in applications, such as physics, biology and chemistry experiments, weather sensors, health sensors, online auctions, and financial streams. Given these developments, the world is poised for a sea change in terms of variety, scale, and importance of applications that can be envisioned based on the real-time analysis and exploitation of such event streams for decision making from dynamic traffic management, environmental monitoring to health care alike. Clearly, the ability to infer relevant patterns from these event streams in real-time to make near instantaneous yet informed decisions, henceforth called complex event analytics (CEA), is crucial for these mission critical applications and this is the focus of our project work. We now list key contribution ideas next.

NEW! Scalable Event Trend Analytics for Data Stream Inquiry

The first contribution tackles the problem of executing queries with Kleene closure. Such queries extract event sequences of arbitrary, statically unknown length, which we call event trends. Due to common event sub-sequences in event trends, either the responsiveness is delayed by repeated computations or an exorbitant amount of memory is required to store partial results. Our solution compactly encodes event trend information using a graph-based data structure and detects trends in optimal CPU time given limited memory.

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Complex Event Analytics

First we notice that existing techniques such as traditional online analytical processing (OLAP) systems are not designed for real-time pattern based operations, while state-of-the-art Complex Event Processing (CEP) systems designed for pattern matching tend to be limited in their expressive capability, and more importantly they do not support OLAP operations. Hence, in the context of event streams where the order and sequence of events are important, OLAP is clearly insufficient in supporting efficient event sequence analysis. Thus in this project, we design, develop and evaluate novel complex event analytics technology that achieves the above objectives.

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