3 accepted papers
In this paper we address the problem of discovering causal relationships from observational event sequence data. Existing methods typically assume that events are instantaneous point events, however in many real-world settings, events have duration. For example, in healthcare, a patient
Understanding causality is challenging and often complicated by changing causal relationships over time and across environments. Climate patterns, for example, shift over time with recurring seasonal trends, while also depending on geographical characteristics such as ecosystem variability. Existing…
Event logs are often one of the main sources of information to understand the behavior of a system. While numerous approaches have extracted partial information from event logs, in this work, we aim at inferring a global model of a system from its event logs. We consider real-time systems, which ca…