The Complex Event Processing Paradigm

Abstract

Complex event processing (CEP) systems represent a mainstream approach for processing streams of data. Specifically, they target the definition and detection of high-level situations of interest, or composite events, starting from streams of primitive events collected from the external environment. In CEP, composite events are specified through user-defined rules, which express how to select, manipulate, and combine primitive events. Thanks to the capability of handling large volumes of information to isolate situations of interest, CEP represents a perfect solution for the management and online analysis of data in pervasive systems. Researchers and practitioners working on CEP focused on the creation of simple, yet expressive languages for the definition of CEP rules. At the same time, they also put significant effort on performance and scalability, defining efficient algorithms and rule evaluation mechanisms that enable high-throughput and low processing delay. This chapter focuses on both aspects. On the one hand, it presents the processing abstractions offered by existing CEP systems in details, focusing on the applicability to pervasive systems. On the other hand, it presents some of the processing algorithms and techniques that contribute to the performance of state-of-the-art CEP systems

Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

“…The rest of the experiments (6)(7)(8)(9)(10)(11) in Table 3 show how averaged values for simultaneity interval and area of location estimation are influenced by alignment and selection algorithm together with different values for simultaneity constraints.…”

“…The data produced by periodic execution of sensor processes (as subscribed by fusion processes) form data streams which can be intermittent 4 with elements not uniformly distributed in time with the possibility of some elements being sporadically delayed due to behavioural pattern of ad hoc WSN. As a result, some of the stream data elements may violate the required validity periods, 3 arrive out of order 6,7 and can often have only partial temporal coverage. 8 This behaviour could be caused by several factors, such as the combined effect from the application of low data rate communication standard (e.g.…”

A Microservices Architecture for Reactive and Proactive Fault Tolerance in IoT Systems

“…properties, errors) then combines them to define and detect a number high-level, complex situations (i.e. new errors) [25].…”

Complex Patterns of Failure: Fault Tolerance via Complex Event Processing for IoT Systems

“…Complex Event Processing (CEP) is used in research and industry to identifying complex, high-level situations (composite events) by defining rule-based patterns in stream data (primitive events). It is considered the paradigm of choice for monitoring and reactive applications [14], [15], making it the ideal platform to provide the FT-support features outlined above. The focus on context awareness and adaptability in IoT calls for novel FT-support approaches that can handle a new range of situational faults that go beyond those found in DS.…”