Abstract.A promising approach to managing business operations is based on business entities with lifecycles (BEL's) (a.k.a. business artifacts), i.e., key conceptual entities that are central to guiding the operations of a business, and whose content changes as they move through those operations. A BEL type includes both an information model that captures, in either materialized or virtual form, all of the business-relevant data about entities of that type, and a lifecycle model, that specifies the possible ways an entity of that type might progress through the business by responding to events and invoking services, including human activities. Most previous work on BEL's has focused on the use of lifecycle models based on variants of finite state machines. This paper introduces the Guard-StageMilestone (GSM) meta-model for lifecycles, which is an evolution of the previous work on BEL's. GSM lifecycles are substantially more declarative than the finite state machine variants, and support hierarchy and parallelism within a single entity instance. The GSM operational semantics are based on a form of EventCondition-Action (ECA) rules, and provide a basis for formal verification and reasoning. This paper provides an informal, preliminary introduction to the GSM approach, and briefly overviews selected research directions.

Big data is recognized as one of the three technology trends at the leading edge a CEO cannot afford to overlook in 2012. Big data is characterized by volume, velocity, variety and veracity ("data in doubt"). As big data applications, many of the emerging event processing applications must process events that arrive from sources such as sensors and social media, which have inherent uncertainties associated with them. Consider, for example, the possibility of incomplete data streams and streams including inaccurate data. In this tutorial we classify the different types of uncertainty found in event processing applications and discuss the implications on event representation and reasoning. An area of research in which uncertainty has been studied is Artificial Intelligence. We discuss, therefore, the main Artificial Intelligence-based event processing systems that support probabilistic reasoning. The presented approaches are illustrated using an example concerning crime detection.

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Uncertainty is inherent in many real-time event-driven applications. Credit card fraud detection is a typical uncertain domain, where potential fraud incidents must be detected in real time and tagged before the transaction has been accepted or denied. We present extensions to the IBM Proactive Technology Online (PROTON) open source tool to cope with uncertainty. The inclusion of uncertainty aspects impacts all levels of the architecture and logic of an event processing engine. The extensions implemented in PROTON include the addition of new built-in attributes and functions, support for new types of operands, and support for event processing patterns to cope with all these. The new capabilities were implemented as building blocks and basic primitives in the complex event processing programmatic language. This enables implementation of eventdriven applications possessing uncertainty aspects from different domains in a generic manner. A first application was devised in the domain of credit card fraud detection. Our preliminary results are encouraging, showing potential benefits that stem from incorporating uncertainty aspects to the domain of credit card fraud detection.

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