Stream Reasoning in Temporal Datalog
This addresses the problem of enabling rule-based reasoning in streaming applications for researchers and practitioners in data processing, though it appears incremental as it builds on existing Datalog extensions.
The paper tackles the challenge of integrating logical reasoning into stream processing by proposing novel reasoning problems for Temporal Datalog, studying their computational properties to enable real-time query answers despite dependencies on past and future data.
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).