Mining Text in Incident Repositories: Experiences and Perspectives on Adopting Machine Learning Solutions in Practice.
Machine learning based data-driven solutions have potential to significantly improve quality of incident management process and make it cost effective. We present our experiences while addressing a spectrum of interrelated problems encountered in practice including identifying semantically related incidents, assignee recommendation, and mapping of incidents to business processes. We argue that despite long-standing research, it is not always straightforward to adopt recommendations from research into practice due to variability and complexity of business constraints and nature of data. We discuss need for meta-level analysis and suggest our own recommendations towards designing pragmatic solutions with low barriers to adoption and addressing right level of challenges.