Process discovery methods automatically infer process models based on events logs that are recorded by information systems. Several heuristic process discovery methods have been proposed to cope with less structured processes and the presence of noise in the event log. However, (1) a large parameter space needs to be explored, (2) it is unclear which heuristics to choose, (3) data attributes are not used for discovery, (4) discovered models are not visualized as described in literature, and (4) there are no quality guarantees for the returned models. We present the interactive Data-aware Heuristics Miner (iDHM), a tool that addresses those four issues. The iDHM enables the interactive exploration of the parameter space and provides built-in conformance checking to diagnose the quality of the discovered model. It is the first tool that visualizes models using the concise Causal Net (C-Net) notation. New heuristic methods can be added through a plug-in architecture, which simplifies their validation and dissemination. We provide a walk-through of the iDHM by applying it to a large event log with hospital billing information. This video show the a screencast of the iDHM walkthrough that was submitted to the demo session of the BPM 2017 conference. Download instructions and a manual of the iDHM are available at: https://fmannhardt.de/g/dhm
