Many stream processing applications require access to a multitude of streaming as well as stored data sources. Yet there is no clear semantics for correct continuous query execution over these data sources in the face of concurrent access and failures. Instead, today's Stream Processing Systems (SPSs) hard-code transactional concepts in their execution models, making them both hard to understand and inflexible to use. In this paper, we show that we can successfully reuse the traditional

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... ional theory (with some minimal extensions) in order to cleanly define the correct interaction of a set of continuous and one-time queries concurrently accessing both streaming and stored data sources. The result is a unified transactional model (UTM) for query processing over streams as well as traditional databases. We present a transaction manager that implements this model on top of an existing storage manager for streams (MXQuery/SMS). Experiments on the Linear Road Benchmark show that our transaction manager flexibly ensures correctness in case of concurrency and failures, without sacrificing from performance. Moreover, this model is powerful enough to express the implicit transactional behaviors of a representative set of state-of-the-art SPSs.