Discovering action idioms bridging the gap between system-level events and human-level actions

As computing devices become more pervasive in our daily lives, effective communication between the user and the system becomes increasingly important. The ability to describe actions at a human level of abstraction is key. However, the level at which computer system events is most easily captured is often well below the level at which humans conceptualize actions. We present a sequential pattern mining approach to discovering human-level actions-action idioms - from instrumentation logs of lower-level events. To support validation by a human expert, idiom discovery is designed to maximize recall, with filtering heuristics applied to help eliminate false positives. Empirical evaluation on data from a fielded application shows the promise of the approach for the automatic discovery of action idioms.