text: The rise of smart applications has drawn interest to logical reasoning over data streams. Recently, different query languages and stream processing reasoning engines were proposed in different communities. However, due to a lack of theoretical foundations, the expressivity and semantics of these diverse approaches are given only informally. Towards clear specifications and means for analytic study, a formal framework is needed to define their semantics in precise terms. To this end, we present a first step towards an ideal semantics that allows for exact descriptions and comparisons of stream reasoning systems.
  @cite_13: There are many academic and commercial stream processing engines (SPEs) today, each of them with its own execution semantics. This variation may lead to seemingly inexplicable differences in query results. In this paper, we present SECRET, a model of the behavior of SPEs. SECRET is a descriptive model that allows users to analyze the behavior of systems and understand the results of window-based queries (with time- and tuple-based windows) for a broad range of heterogeneous SPEs. The model is the result of extensive analysis and experimentation with several commercial and academic engines. In the paper, we describe the types of heterogeneity found in existing engines and show with experiments on real systems that our model can explain the key differences in windowing behavior.
summary: SECRET @cite_13 a model called SECRET is proposed to analyze the execution behavior of different stream processing engines (SPEs) from a practical point of view. The authors found that even the outcome of identical, simple queries vary significantly due to the different underlying processing models. There, the focus is on understanding, comparing and predicting the . In contrast, we want to provide means that allow for a similar analytical study for the of stream reasoning formalisms and engines. The two approaches are thus orthogonal and can be used together to compare stream reasoning engines based on different input feeding modes as well as different reasoning expressiveness.