Workflow extraction for service operation using multiple unstructured trouble tickets
Abstract
In current large scale networks, troubleshooting has become more complicated task due to the diversification in the causes of network failures. The increase in the operational costs has become a serious problem. Thus, manualization of the troubleshooting process also becomes important task though it is time-consuming. We propose a method that automatically extracts a workflow for troubleshooting using multiple trouble tickets. Our method extracts an operator's actions from free-format texts and aligns relative sentences between multiple trouble tickets. Finally, we show a novel approach to visualizing a workflow by mining conditional branches using clustering. We validated our method using real trouble ticket data captured from a network operation and showed that it can extract the workflow to identify the cause of failure.
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Watanabe et al. [470] report a precision of over 83% using real trouble tickets obtained from an enterprise service.
“Using real trouble tickets, obtained from an enterprise service, the authors report a precision of over 83%.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [470]
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1 of the 19 references couldn’t be matched to a known paper.
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“[470] propose a method for automatically extracting a workflow from unstructured trouble tickets to troubleshoot a network fault.”
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Ikeuchi, H., Ge, J., Matsuo, Y., & Watanabe, K. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00170 · Cited by 12 · see citing works
“Some methods [4]–[6] are used to analyze trouble tickets, which are historical reports on the recovery process written in a natural language, and visualize them as a workflow that suggests recovery actions for operators to take.”
MethodSelf-citationSato, T., Shimosawa, T., & Himura, Y. (2022) · IEICE Transactions on Communications · doi:10.1587/transcom.2021tmp0008 · Cited by 12 · see citing works
“[12][13] shows a method that discovers a history of operations by automatically analyzing raw system logs.”
MethodWorkflow Extraction for Service Operation Using Multiple Unstructured Trouble Tickets
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Self-citationRecovery command generation towards automatic recovery in ICT systems by Seq2Seq learning
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“Other methods [8]–[10] analyze multiple trouble tickets (or official troubleshooting guidelines if any) and visu-”
MethodSelf-citationOpsSC: Decentralized Blockchain Network Operation Workflow for Hyperledger Fabric
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Application of AI to Network Operation
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“[20] shows a method that extracts operation workflows by analyzing text-based working histories for trouble tickets.”
MethodGlisic, S., & Lorenzo, B. (2022) · Wiley eBooks · doi:10.1002/9781119790327.ch7
- Authors
- Watanabe, A., Ishibashi, K., Toyono, T., Kimura, T., Watanabe, K., Matsuo, Y., & Shiomoto, K.
- Affiliations
- NTT (Japan) (JP)
- Venue
- NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium
- Publisher
- IEEE
- Published
- 2016-04
- Pages
- 652-658
- OpenAlex
- W2473285914
- Cited by
- 11
- References
- 19
- Citations by year
- 2022: 2 · 2021: 1 · 2020: 3 · 2019: 1 · 2018: 4
- FWCI
- 1.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/IFIP Network Operations and Management Symposium (NOMS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Social Sciences › Business, Management and Accounting › Management Information Systems › Business Process Modeling and Analysis
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Robotic Process Automation Applications
- Keywords
- network troubleshooting, service operations, unstructured text, operator actions, sentence alignment, clustering, workflow visualization, failure cause identification, network operation
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