Abstract
The ever-increasing attention of process mining (PM) research to the logs of low structured processes and of non-process-aware systems (e.g., ERP, IoT systems) poses a number of challenges. Indeed, in such cases, the risk of obtaining low-quality results is rather high, and great effort is needed to carry out a PM project, most of which is usually spent in trying different ways to select and prepare the input data for PM tasks. Two general AI-based strategies are discussed in this paper, which can improve and ease the execution of PM tasks in such settings: (a) using explicit domain knowledge and (b) exploiting auxiliary AI tasks. After introducing some specific data quality issues that complicate the application of PM techniques in the above-mentioned settings, the paper illustrates these two strategies and the results of a systematic review of relevant literature on the topic. Finally, the paper presents a taxonomical scheme of the works reviewed and discusses some major trends, open issues and opportunities in this field of research.
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