Event processing in the form of ECA rules has been researched extensively from the situation monitoring viewpoint to detect changes in a timely manner and to take appropriate actions. Several event specification languages and processing models have been developed, analyzed, and implemented. More recently, data stream processing has been receiving a lot of attention to deal with applications that generate large amounts of data in real-time at varying input rates and to compute functions over multiple streams that satisfy quality of service (QoS) requirements. A few systems based on the data stream processing model have been proposed to deal with change detection and situation monitoring. However, current data stream processing models lack the notion of composite event specification and computation, and they cannot be readily combined with event detection and rule specification, which are necessary and important for many applications. This chapter discusses a couple of representative scenarios that require both stream and event processing. The authors then summarize the similarities and differences between the event and data stream processing models. The comparison clearly indicates that for most of the applications considered for stream processing, event component is needed and is not currently supported. And conversely, earlier event processing systems assumed primitive (or simple) events triggered by DBMS and other applications and did not consider computed events. By synthesizing these two and combining their strengths, the authors present an integrated model – one that will be better than the sum of its parts. The authors discuss the notion of a semantic window, which extends the current window concept for continuous queries, and stream modifiers in order to extend current stream computation model for complicated change detection. They further discuss the extension of event specification to include continuous queries. Finally, the authors demonstrate how one of the scenarios discussed earlier can be elegantly and effectively modeled using the integrated approach.
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Event processing (Dayal et al., 1988; Schreier et al., 1991; Diaz, Paton, & Gray, 1991; Gehani, Jagadish, & Shmueli, 1992a; Gatziu & Dittrich, 1993; Kotz-Dittrich, 1993; Buchmann et al., 1993; Chakravarthy, Anwar, Maugis, & Mishra, 1994; Hanson, 1996; Lieuwen, Gehani, & Arlein, 1996; Seshadri, Livny, & Ramakrishnan, 1996; Engstrom, Berndtsson, & Lings, 1997; Dinn, Williams, & Paton, 1997) and lately data stream processing (Babu & Widom, 2001; Abadi et al., 2003; Chen et al., 2000; Madden & Franklin, 2002; Jiang & Chakravarthy, 2004a) have evolved independently based on situation monitoring application needs. Triggers have been successfully defined over relational databases and several event specification languages (Gehani, Jagadish, & Shmueli, 1992b, 1992c; Gatziu & Dittrich, 1993, 1994; Chakravarthy & Mishra, 1994; Roncancio, 1997; Adaikkalavan & Chakravarthy, 2003) for specifying composite events have been proposed. Different computation models (Gehani & Jagadish, 1991; Lieuwen et al., 1996; Gatziu & Dittrich, 1992, 1993; Engstrom et al., 1997; Buchmann et al., 1993; Chakravarthy et al., 1994; Dinn et al., 1997) for processing events, such as Petri nets (Gatziu & Dittrich, 1992, 1993), extended automata (Gehani & Jagadish, 1991; Lieuwen et al., 1996; Gehani et al., 1992c), and event graphs (Buchmann et al., 1993; Chakravarthy et al., 1994; Engstrom et al., 1997) – have been proposed and implemented. Various event consumption modes (Buchmann et al., 1993; Gatziu & Dittrich, 1992, 1993; Chakravarthy et al., 1994; Chakravarthy & Mishra, 1994) (or parameter contexts) have been explored. Similarly, data stream processing has received a lot of attention lately, and a number of issues – from architecture (Abadi et al., 2003; Madden & Franklin, 2002; Jiang & Chakravarthy, 2004a; Chen et al., 2000; Motwani et al., 2003) to quality of service (Tatbul et al., 2003; Babcock, Datar, & Motwani, 2004; Das, Gehrke, & Riedewald, 2003; Jiang & Chakravarthy, 2004b; Brian et al., 2003; Carney et al., 2003) – have been explored. Although both of these topics seem different on the face of it, we argue that there are a number of similarities and some differences between them. Surprisingly, the computation model used for data stream processing is not very dissimilar from some of the event processing models (e.g., event graph), but with a different emphasis.