A Formal Comparison between Datalog-based Languages for Stream Reasoning (extended version)

TL;DR

This paper compares the expressiveness of two Datalog-based stream reasoning languages, LARS and LDSR, establishing their incomparability and identifying fragments where they can simulate each other.

Contribution

It introduces a formal comparison framework for LARS and LDSR, clarifying their relative expressiveness and fragment equivalences.

Findings

LARS and LDSR are generally incomparable in expressiveness.

Certain fragments of LARS can be expressed in LDSR and vice versa.

The comparison framework helps understand the capabilities and limitations of each language.

Abstract

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.