{'fraction_non_alphanumeric': 0.019347037484885126, 'fraction_numerical': 0.0, 'mean_word_length': 5.46875, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Ex-Post Identification of Task Models With Causally Ordered User Interface Logs (Extended Abstract)

Identifying adequate back-office tasks to automate is a significant problem in adopting robotic process automation. Task mining on user interface logs enables departments to detect the underlying task model, but creating these logs is time and resource-consuming. Furthermore, contrary to process mining logs that can be extracted in an ex-post fashion from ERP systems, user interface logs require definitions prior to the recording: abstract activities and mapping to lower-level user interface interactions need to be specified beforehand. To this end, the presented research project proposes a new framework for desktop activity mining that enables interaction recording prior to defining tasks and activities to be mined.