Complex Event Processing (CEP) has emerged as the unifying field for technologies that require processing and correlating heterogeneous distributed data sources in real-time. CEP finds applications in diverse domains, which has resulted in a large number of proposals for expressing and processing complex events. However, existing CEP frameworks are based on ad-hoc solutions that do not rely on solid theoretical ground, making them hard to understand, extend or generalize. Moreover, they are usually presented as application programming interfaces documented by examples, and using each of them requires learning a different set of skills. In this paper we embark on the task of giving a rigorous framework to CEP. As a starting point, we propose a formal language for specifying complex events, called CEPL, that contains the common features used in the literature and has a simple and denotational semantics. We also formalize the so-called selection strategies, which are the cornerstone of CEP and had only been presented as by-design extensions to existing frameworks. With a well-defined semantics at hand, we study how to efficiently evaluate CEPL for processing complex events. We provide optimization results based on rewriting formulas to a normal form that simplifies the evaluation of filters. Furthermore, we introduce a formal computational model for CEP based on transducers and symbolic automata, called match automata, that captures the regular core of CEPL, i.e. formulas with unary predicates. By using rewriting techniques and automata-based translations, we show that formulas in the regular core of CEPL can be evaluated using constant time per event followed by constant-delay enumeration of the output (under data complexity). By gathering these results together, we propose a framework for efficiently evaluating CEPL, establishing solid foundations for future CEP systems. Foundations of Complex Event Processing

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