Analyzing Comments in Ticket Resolution to Capture Underlying Process Interactions

Monika Gupta, Prerna Agarwal, Tarun Tater, Sampath Dechu, Alexander Serebrenik

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Activities in the ticket resolution process have comments and emails associated with them. Process mining uses structured logs and does not analyze the unstructured data such as comments for process discovery. However, comments can provide additional information for discovering models of process reality and identifying improvement opportunities efficiently. To address the problem, we propose to extract topical phrases (keyphrases) from the unstructured data using an unsupervised graph-based approach. These keyphrases are then integrated into the event log to derive enriched event logs. A process model is discovered using the enriched event logs wherein keyphrases are represented as activities, thereby capturing the flow relationship with other activities and the frequency of occurrence. This provides insights that can not be
obtained solely from the structured data.

To evaluate the approach, we conduct a case study on the ticket data of a large global IT company. Our approach extracts keyphrases with an average accuracy of around 80%. Henceforth, discovered process model succinctly captures underlying process interactions which allows to understand in detail the process realities and identify opportunities for improvement. In this case, for example, manager identified that having a bot to capture specific information can reduce the delays incurred while waiting for the information.
Original languageEnglish
Title of host publication4th International Workshop in Artificial Intelligence for Business Process Management
PublisherSpringer
Pages219-231
Volume397
Publication statusPublished - 5 Mar 2021

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