Many modern operational performance environments produce significant data artifacts that collectively constitute rich libraries of decision-making examples. For domains where expert decisions are guided by constraints, there is the potential to automatically derive the constraints themselves from expert performance data. This paper discusses a datadriven machine learning approach to modeling constraints, implemented in an authoring tool coupled with a simulation-based training environment for satellite planners. In this domain, the planner's task is to create a 7-day schedule of requested satellite contacts, while meeting a range of specialized planning constraints which vary for different satellites with different missions. The training goal is to assess planners' decisions in simulation-based scenarios and provide feedback, which requires automated performance assessment measures with knowledge of planning constraints. For this application, the authoring tool provides a utility to directly process operational source data, in this case consisting of archived records of satellite requests from previous periods. This produces derived constraints, which authors then review, edit, and annotate as needed before linking the constraints to runtime assessment mechanisms for exercises. Beyond the initial focus on generating automated assessment with this datadriven approach, the development process uncovered other useful applications for the ability to derive constraints from operational data. For example, one phase of the satellite planning process involves deconflicting one's own satellite support requests from those involving other satellites that may be seeking to simultaneously use the same resources, such as a specific ground antenna. In order to support individual training for this task, an automated agent was created to produce realistic simulated conflicts with the planner's requests, based on constraints mined from operational data. This research also helped to uncover drawbacks in the data-driven approach for some domains, so this paper discusses applicability and limitations in more general cases beyond the initial satellite planning application.