event simulation, the active database area and some programming languages. The activity in the industry was preceded by a wave of research projects in the 1990s. According to the first project that paved the way to a generic CEP language and execution model was the Rapide project in Stanford University, directed by David Luckham. In parallel there have been two other research projects: Infospheres in California Institute of Technology, directed by K. Mani Chandy, and Apama in University of Cambridge directed by John Bates. The commercial products were dependents of the concepts developed in these and some later research projects. Community efforts started in a series of event processing symposiums organized by the Event Processing Technical Society, and later by the ACM DEBS conference series. One of the community efforts was to produce the event processing manifesto. Related concepts CEP is used in operational intelligence (OI) products to provide insight into business operations by running query analysis against live feeds and event data. OI collects real-time data and correlates against historical data to provide insight and analysis. Multiple sources of data can be combined to provide a common operating picture that uses current information. In network management, systems management, application management and service management, people usually refer instead to event correlation. As CEP engines, event correlation engines (event correlators) analyze a mass of events, pinpoint the most significant ones, and trigger actions. However, most of them do not produce new inferred events. Instead, they relate high-level events with low-level events. Inference engines, e.g., rule-based reasoning engines, typically produce inferred information in artificial intelligence. However, they do not usually produce new information in the form of complex (i.e., inferred) events. Example A more systemic example of CEP involves a car, some sensors and various events and reactions. Imagine that a car has several sensors—one that measures tire pressure, one that measures speed, and one that detects if someone sits on a seat or leaves a seat. In the first situation, the car is moving and the pressure of one of the tires moves from 45 psi to 41 psi over 15 minutes. As the pressure in the tire is decreasing, a series of events containing the tire pressure is generated. In addition, a series of events containing the speed of the car is generated. The car's Event Processor may detect a situation whereby a loss of tire pressure over a relatively