LARS: A Logic-Based Framework for Analyzing Reasoning over Streams


  • Harald Beck Vienna University of Technology
  • Minh Dao-Tran Vienna University of Technology
  • Thomas Eiter Vienna University of Technology
  • Michael Fink Vienna University of Technology



Knowledge Representation and Reasoning, Answer Set Programming, Stream Reasoning, Nonmonotonic Reasoning


The recent rise of smart applications has drawn interest to logical reasoning over data streams. Different query languages and stream processing/reasoning engines were proposed. However, due to a lack of theoretical foundations, the expressivity and semantics of these diverse approaches were only informally discussed. Towards clear specifications and means for analytic study, a formal framework is needed to characterize their semantics in precise terms. We present LARS, a Logic-based framework for Analyzing Reasoning over Streams, i.e., a rule-based formalism with a novel window operator providing a flexible mechanism to represent views on streaming data. We establish complexity results for central reasoning tasks and show how the prominent Continuous Query Language (CQL) can be captured. Moreover, the relation between LARS and ETALIS, a system for complex event processing is discussed. We thus demonstrate the capability of LARS to serve as the desired formal foundation for expressing and analyzing different semantic approaches to stream processing/reasoning and engines.




How to Cite

Beck, H., Dao-Tran, M., Eiter, T., & Fink, M. (2015). LARS: A Logic-Based Framework for Analyzing Reasoning over Streams. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



AAAI Technical Track: Knowledge Representation and Reasoning