Dependency Detection in Interval-based Event Streams

We present a new approach to mine dependencies between streams of interval-based events that links two events if they occur in a similar manner, one being often followed by the other one in the data. The proposed technique is robust to temporal variability of events and determines the most appropriate time intervals whose validity is assessed by a χ test. TEDDY algorithm prunes the search space while certifying the discovery of all valid and significant temporal dependencies.