Abstract
The ever increasing attention of Process Mining (PM) research to the logs of lowly-structured processes and of non process-aware systems (e.g., ERP, IoT systems) poses several challenges stemming from the lower quality that these logs have, concerning the precision, completeness and abstraction with which they describe the activities performed. In such scenarios, most of the resources spent in a PM project (in terms of time and expertise) are usually devoted to try different ways of selecting and preparing the input data for PM tasks, in order to eventually obtain significant, interpretable and actionable results. Two general AI-based strategies are discussed here that have been partly pursued in the literature to improve the achievements of PM efforts on low-quality logs, and to limit the amount of human intervention needed: (i) using explicit domain knowledge, and (ii) exploiting auxiliary AI tasks. The also provides an overview of trends, open issues and opportunities in the field.
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