Log2model: inferring behavioral models from log data

Abstract

We present LOG2MODEL, an approach, supported by a tool, that builds behavioral models from log data. The logged data consists of time series encoding the values of the states of a system observed at discrete time steps. The models generated are Discrete-Time Markov Chains with states and transitions representing the values recorded in the log. The models contain key information that can be visualized and analyzed with respect to safety, delays, throughput etc, using off-the-shelf model checkers such as PRISM. The analysis results can be further used by users or automated tools to monitor and alter the system behavior. We present the architecture of LOG2MODEL and its application in the context of autonomous operations in the airspace domain.