Mining Temporal Intervals from Real-time System Traces

We introduce a novel algorithm for mining temporal intervals from real-time system traces with linear complexity using passive, black-box learning. Our interest is in mining nfer specifications from spacecraft telemetry to improve human and machine comprehension. Nfer is a recently proposed formalism for inferring event stream abstractions with a rule notation based on Allen Logic. The problem of mining Allen s relations from a multivariate interval series is well studied, but little attention has been paid to generating such a series from symbolic time sequences such as system traces. We propose a method to automatically generate an interval series from real-time system traces so that they may be used as inputs to existing algorithms to mine nfer rules. Our algorithm has linear runtime and constant space complexity in the length of the trace and can mine infrequent intervals of arbitrary length from incomplete traces. The paper includes results from case studies using logs from the Curiosity rover on Mars and two other realistic datasets.