Computer Science Department
School of Computer Science, Carnegie Mellon University
Eliciting User Expectations for Data Behavior
Orna Raz, Rebecca Buchheit, Mary Shaw
This research uses machine learning techniques to refine the model and accommodates the necessary human participation. We propose a template mechanism to bridge between user expectations and techniques output. The result is an analyzable model of proper behavior that may serve as a proxy for missing specifications. We use the model to detect semantic anomalies---data behavior that is outside the user's expectations.
We test our template mechanism on truck weigh-in-motion (WIM) data. A domain expert interacts with this mechanism to set up the model of proper behavior. We then analyze the usefulness of this model for anomaly detection and show it is useful. We also compare the model to existing documentation and show how this gave the expert insights about the WIM system.