A Formal Comparison between Datalog-based Languages for Stream Reasoning (extended version)
This work addresses the problem of reconciling expressive capabilities in stream reasoning languages for researchers and practitioners in logic-based AI, though it is incremental as it focuses on theoretical comparison rather than new applications.
The paper investigates the relative expressiveness of two Datalog-based languages for stream reasoning—LARS Programs and LDSR—by developing a comparison framework. It finds that without restrictions, the languages are incomparable, but identifies specific fragments of each that can be expressed via the other.
The paper investigates the relative expressiveness of two logic-based languages for reasoning over streams, namely LARS Programs -- the language of the Logic-based framework for Analytic Reasoning over Streams called LARS -- and LDSR -- the language of the recent extension of the I-DLV system for stream reasoning called I-DLV-sr. Although these two languages build over Datalog, they do differ both in syntax and semantics. To reconcile their expressive capabilities for stream reasoning, we define a comparison framework that allows us to show that, without any restrictions, the two languages are incomparable and to identify fragments of each language that can be expressed via the other one.