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Recent years witnessed a rising interest towards Datalog-based ontologicalreasoning systems, both in academia and industry. These systems adoptlanguages, often shared under the collective name of Datalog$+/-$, that extendDatalog with the essential feature of existential quantification, whileintroducing syntactic limitations to sustain reasoning decidability and achievea good trade-off between expressive power and computational complexity. From animplementation perspective, modern reasoners borrow the vast experience of thedatabase community in developing streaming-based data processing systems, suchas volcano-iterator architectures, that sustain a limited memory footprint andgood scalability. In this paper, we focus on two extremely promising,expressive, and tractable languages, namely, Shy and Warded Datalog$+/-$. Weleverage their theoretical underpinnings to introduce novel reasoningtechniques, technically, "chase variants", that are particularly fit forefficient reasoning in streaming-based architectures. We then implement them inVadalog, our reference streaming-based engine, to efficiently solve ontologicalreasoning tasks over real-world settings.

Ontological Reasoning over Shy and Warded Datalog$+/-$ for Streaming-based Architectures (technical report)