# Incident-aware Duplicate Ticket Aggregation for Cloud Systems

arXiv: https://arxiv.org/abs/2302.09520 · HTML: https://mlsanity.com/paper/2302.09520/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2023.

## Built on by (most cited first, 5 of 5)

- [Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides](https://mlsanity.com/paper/2402.17531/) (2024)
- [Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach](https://mlsanity.com/paper/2403.06485/) (2024)
- [FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems](https://mlsanity.com/paper/2402.17583/) (2024)
- [Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services](https://mlsanity.com/paper/2308.09937/) (2023)
- [FixItFlow: Automated Troubleshooting Guide Generation from Cloud Incidents](https://mlsanity.com/paper/2607.13035/) (2026)

## Abstract (arXiv)

In cloud systems, incidents are potential threats to customer satisfaction and business revenue. When customers are affected by incidents, they often request customer support service (CSS) from the cloud provider by submitting a support ticket. Many tickets could be duplicate as they are reported in a distributed and uncoordinated manner. Thus, aggregating such duplicate tickets is essential for efficient ticket management. Previous studies mainly rely on tickets' textual similarity to detect duplication; however, duplicate tickets in a cloud system could carry semantically different descriptions due to the complex service dependency of the cloud system. To tackle this problem, we propose iPACK, an incident-aware method for aggregating duplicate tickets by fusing the failure information between the customer side (i.e., tickets) and the cloud side (i.e., incidents). We extensively evaluate iPACK on three datasets collected from the production environment of a large-scale cloud platform, Azure. The experimental results show that iPACK can precisely and comprehensively aggregate duplicate tickets, achieving an F1 score of 0.871 ~ 0.935 and outperforming state-of-the-art methods by 12.4% ~ 31.2%.