Stream Reasoning in Temporal Datalog

TL;DR

This paper explores the challenges and computational properties of extending Datalog with temporal reasoning capabilities for stream processing, addressing issues like delayed data and limited memory in real-time query answering.

Contribution

It introduces novel reasoning problems and analyzes their computational aspects within a temporal Datalog framework for stream reasoning.

Findings

Identifies key challenges in temporal stream reasoning.

Analyzes computational complexity of temporal Datalog extensions.

Proposes formal reasoning problems for stream processing applications.

Abstract

In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications).