My second afternoon session was formed by paper 506, 553, and 582. Machine learning was the pervasive fabric for this session. From bayesian network modeling for network intrusion detection and machine learning for process mining, to music information retrieval and similarity measures. All of them were interesting on their own, but I would like to write down some of the thoughts about paper 553. The paper focussed on process mining. Basically from a log of a process they reconstructed the events and graph and tried to induce the business rules governing the process. They authors took a divide and conquer approach to simplify the modeling inside smaller regions of the problem—very similar to the GALE approach back in 1999, or the mixed decision trees paper. After the talk was over I kept wondering where is the connection between process mining and provenance mining—provenance can be defined as the execution history of computer processes which were utilized to compute a final piece of data. Quite an intriguing thought.