This chapter introduces PADRES, the publish/subscribe model with the capability to correlate events, uniformly access data produced in the past and future, balance the traffic load among brokers, and handle network failures. The new model can filter, aggregate, correlate and project any combination of historic and future data. A flexible architecture is proposed consisting of distributed and replicated data repositories that can be provisioned in ways to tradeoff availability, storage overhead, query overhead, query delay, load distribution, parallelism, redundancy and locality. This chapter gives a detailed overview of the PADRES content-based publish/subscribe system. Several applications are presented in detail that can benefit from the content-based nature of the publish/subscribe paradigm and take advantage of its scalability and robustness features. A list of example applications are discussed that can benefit from the content-based nature of publish/subscribe paradigm and take advantage of its scalability and robustness features.
TopIntroduction
The publish/subscribe paradigm provides a simple and effective method for disseminating data while maintaining a clean decoupling of data sources and sinks (Cugola, 2001; Fabret, 2001; Castro, 2002;Fiege, 2002; Carzaniga, 2003; Eugster, 2003; Li, 2005; Ostrowski, 2006; Rose, 2007). This decoupling can enable the design of large, distributed, and loosely coupled systems that interoperate through simple publish and subscribe invocations. While there are many applications such as information dissemination (Liu, 2004; Nayate, 2004; Liu, 2005) based on group communication (Birman, 1999) and topic-based publish/subscribe protocols (Castro, 2002; Ostrowski, 2006), a large variety of emerging applications benefit from the expressiveness, filtering, distributed event correlation, and complex event processing capabilities of content-based publish/subscribe systems. These applications include RSS feed filtering (Rose, 2007), stock-market monitoring engines (Tock, 2005), system and network management and monitoring (Mukherjee, 1994; Fawcett, 1999), algorithmic trading with complex event processing (Keonig, 2007), business process management and execution (Schuler, 2001; Andrews, 2003;), business activity monitoring (Fawcett, 1999), workflow management (Cugola, 2001), and service discovery (Hu, 2008).