Mining Text in Incident Repositories: Experiences and Perspectives on Adopting Machine Learning Solutions in Practice Machine learning based datadriven 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 longstanding 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 metalevel analysis and suggest our own recommendations towards designing pragmatic solutions with low barriers to adoption and addressing right level of challenges.

Attempt to Extract Similar Cases by Applying Multilayer Perceptron to a Failure Database Networked information systems have become indispensable infrastructure in various fields of society. Although advances in IT have provided great convenience, IT can cause failures that have significant negative impacts. Under these circumstances, it is important to accumulate, analyze, and use many past failure cases to prevent recurrence of failures and prevent new failures. In order to achieve this goal, the authors are conducting research to apply machine learning to a failure database accumulated in the past. In previous research, we applied bagofwords (BoW) and topic models as machine learning models. At present, we are applying a multilayer perceptron (MLP) to extract past failure cases similar to a query. In the present paper, we show experimental results for the method applying a MLP and compare the results with these obtained by the conventional bagofwords and topic models.