A model for complex event processing, 2009

Advances in sensor technology will revolutionize the way that real-world events are collected and interpreted. The ability to ubiquitously capture data will generate an unprecedented amount of data making distributed data management and decision making key challenges in the deployment of this technology. The demands for intelligently managing real-time data and integrating it into applicable business processes have propelled the emergence of a new breed of distributed software systems. The challenges are broader than simply creating a software platform to manage and integrate the sheer volume of sensor data. Mechanisms that permit the application of contextual and application knowledge into the distributed decision making infrastructure are required. The design of such software is based on the theory of event which permits events to be states, or processes. In managing real-time data and information from distributed heterogeneous sensors, the notion of the event is attractive for several reasons. First, modeling data in terms of events parallels the way humans conceptualize and relate information. Second, the notion of events, especially the differentiation between significant and non-significant 1 events may be used to filter data. Third, the definition of an event provides an implicit data wrapper may be used to link sensor data through event relationships. These relationships may be used to reason in an enterprise application context. Finally, the event-based approach is well suited to associating autonomous, heterogeneous sensor nodes by means of the inherent properties of events such as time and space. Thus these sensor nodes may be integrated into a complex decision making networks through eventbased communication. In this thesis, the design and development of a distributed software platform which can acquire data from heterogeneous sensors, integrate, and provide distributed decision support is described. Raw data is processed at multiple levels of abstraction and using context infonnation combined to form higher-level events that enable real time decision making. A multi-layered event representation and reasoning model is implemented that feeds sensory data derived from low level sensors into higher-level event structures. Then, it can be exploited by appropriate event handlers. Alternate approaches to the sense making problem are discussed and the advantages of the proposed model is explained