Discovering Business Processes in CRM Systems by leveraging unstructured text data

Recent research has proven the feasibility of using Process Mining algorithms to discover business processes from event logs of structured data. However, many IT systems also store a considerable amount of unstructured data. Customer Relationship Management (CRM) Systems typically store information about interactions with customers, such as emails, phone calls, meetings, etc. These activities are characteristically made up of unstructured data, such as a free text subject and description of the interaction, but only limited structured data is available to classify them. This poses a problem to the traditional Process Mining approach that relies on an event log made up of clearly categorised activities. This paper proposes an original framework to mine processes from CRM data, by leveraging the unstructured part of the data. This method uses Latent Dirichlet Allocation (LDA), an unsupervised machine learning technique, to automatically detect and assign labels to activities. This framework does not require any human intervention. A case study with real-world CRM data validates the feasibility of our approach

Discovering Process Models from Patient Notes

Process Mining typically requires event logs where each event is labelled with a process activity. That’s not always the case, as many process-aware information systems store process-related information in the form of text notes. An example are patient information systems (PIS), which store much information in the form of free-text patient notes. Labelling text-based events with their activity is not trivial, because of the amount of data involved, but also because the activity represented by a text note can be am-biguous. Depending on the requirements of a process analyst, we might need to label events with more or fewer unique activities: two similar events could represent the same activity (e.g. screen referral) or two different activities (e.g. screen adult ADHD referral and screen depression referral). We can therefore view activities as ontologies with an arbitrary number of entries. This paper proposes a method that produces an ontology for the activities of a process by analysing a text-based event log. We implemented an interactive tool that generates process models based on this ontology and the text-based event log. We demonstrate the proposed method’s usefulness by dis-covering a mental health referral process model from real-world data