This dataset contains the experimental results from the feasibility study of the MAPKIP approach. MAPKIP is designed to support the modeling of knowledge-intensive processes (KiPs) by automatically generating process models in the form of executable plans. These plans are dynamically derived at runtime by translating an artifact-centric case model, defined using the METAKIP metamodel, into a planning model. The planning model is based on Markov Decision Processes (MDPs), which are well-suited to capture the inherent uncertainty of KiPs. To construct and analyze the MDPs, we use the probabilistic model checker PRISM. The dataset includes seven scenarios that model hypertension treatment processes for specific case instances, each representing a different level of complexity. For each scenario, the corresponding folder contains: <ul> <li> The MDP model (model.prism), encoded in the PRISM language, represents the planning problem (case instance) and domain (case model). </li> <li> The property file (properties.props) specifying the goal state </li> <li> Original output files generated by PRISM, including execution logs(runner.log), the transition matrix file (output.tra), the state file (output.sta) containing reachable states, the label file (output.lab), and the transition matrix in dot format (output.dot) </li> <li> The visualization of the generated plan (output.png), produced using GraphViz</li> </ul> The evaluation was conducted on a system equipped with an Intel Core i5 processor (2.8 GHz) and 4 GB of RAM.