Processing Flows of Information: From Data Stream to Complex Event Processing

A large number of distributed applications requires continuous and timely processing of informa-tion as it flows from the peripheral to the center of the system. Examples are intrusion detection systems, which analyze network traffic in real-time to identify possible attacks; environmental monitoring applications, which process raw data coming from sensor networks to identify criti-cal situations; or applications performing on-line analysis of stock prices to identify trends and forecast future values. Traditional DBMSs, with the need of storing and indexing data before processing it, have an hard time in fulfilling the requirement of timeliness coming from such domains. Accordingly, during the last decade different research communities, often working in isolation with each other, developed a number of tools, which we collectively call Information Flow Processing (IFP) Sys-tems, to support these scenarios. They differ in their system architecture, data model, rule model, and rule language. In this paper we survey these systems to help researchers, often coming from different backgrounds, in understanding how the different approaches may complement each other. In particular, we propose a general, unifying model to capture the different aspects of an IFP system and use it to provide a complete and precise classification of the different systems and mechanisms proposed so far