In the last decade, there has been a growing interest in applying AI technologies to
implement complex data analytics over data streams. To this end, researchers in various …

We introduce a novel logic-based system for reasoning over data streams, which relies on a
framework enabling a tight, fine-tuned interaction between Apache Flink and the-DLV …

We study stream reasoning in DatalogMTL—an extension of Datalog with metric temporal
operators. We propose a sound and complete stream reasoning algorithm that is applicable …

The repeated execution of reasoning tasks is desirable in many applicative scenarios, such
as stream reasoning and event processing. When using answer set programming in such …

In the latest years, Stream Reasoning (SR) has become increasingly relevant in various
scenarios where it is required to reason over heterogeneous and highly dynamic data …

Contemporary applications require the processing of large, high-velocity streams of
symbolic events derived from sensor data. A complex event recognition (CER) system …

Efficient decision-making over continuously changing data is essential for many application
domains such as cyber-physical systems, industry digitalization, etc. Modern stream …

Temporal specifications, such as those of multi-agent systems, often include cyclic
dependencies. Moreover, there is an increasing need to evaluate such specifications in an …

Stream Reasoning (SR) focuses on develo** advanced approaches for applying
inference to dynamic data streams; it has become increasingly relevant in various …

Stream reasoning allows us to draw conclusions in a temporal domain with data that can
change at different time points. By using a stream reasoning framework with syntax and …