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Incident Memory Graphs for Predictive AMS Triage and Self-Improving SAP Operations

Abstract

Application Management Services (AMS) teams repeatedly resolve similar enterprise incidents despite possessing extensive historical knowledge embedded within service management repositories. This paper introduces Incident Memory Graphs, a graph-based knowledge architecture that captures relationships among incidents, root causes, system configurations, business processes, and remediation strategies to enable predictive support operations. The proposed framework constructs continuously evolving enterprise knowledge graphs using historical incident records, SAP change history, transport logs, monitoring events, and operational documentation. Machine learning algorithms leverage graph embeddings to recommend probable root causes, remediation strategies, and expert assignments for newly reported incidents. Reinforcement learning continuously refines recommendation accuracy as operational experience grows

References

[1]. Al-Hawari, F., & Barham, H. (2021). A machine learning based help desk system for IT service management. Journal of King Saud University , Computer and Information Sciences, 33(6), 702, 718. https://doi.org/10.1016/j.jksuci.2019.03.001
[2]. Angles, R., & Gutiérrez, C. (2008). Survey of graph database models. ACM Computing Surveys, 40(1), 1, 39. https://doi.org/10.1145/1322432.1322433
[3]. AXELOS. (2019). ITIL Foundation: ITIL 4 Edition. AXELOS Ltd.
[4]. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems, 26, 2787, 2795.
[5]. Brandón, Á., Solé, M., Huélamo, A., Solans, D., Pérez, M. S., & Muntés-Mulero, V. (2020). Graph-based root cause analysis for service-oriented and microservice architectures. Journal of Systems and Software, 159, 110432. https://doi.org/10.1016/j.jss.2019.110432
[6]. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), Article 15. https://doi.org/10.1145/1541880.1541882
[7]. Chen, C., Fu, H., Zheng, Y., Tao, F., & Liu, Y. (2023). The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems, 71, 581, 594. https://doi.org/10.1016/j.jmsy.2023.10.010
[8]. Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computers in Industry, 123, 103298. https://doi.org/10.1016/j.compind.2020.103298
[9]. Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Harvard Business School Press.
[10]. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108, 116.
[11]. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171, 4186.
[12]. Ding, R., Zhou, H., Lou, J.-G., Zhang, H., Lin, Q., Fu, Q., Zhang, D., & Xie, T. (2023). TraceDiag: Adaptive, interpretable and efficient root cause analysis on large-scale microservice systems. Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 1762, 1773. https://doi.org/10.1145/3611643.3613864
[13]. Du, M., Li, F., Zheng, G., & Srikumar, V. (2017). DeepLog: Anomaly detection and diagnosis from system logs through deep learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS), 1285, 1298. https://doi.org/10.1145/3133956.3134015
[14]. Ehrlinger, L., & Wöß, W. (2016). Towards a definition of knowledge graphs. Proceedings of the 12th International Conference on Semantic Systems (SEMANTiCS 2016), CEUR Workshop Proceedings, 1695.
[15]. Fuchs, C., Drieschner, C., & Wittges, H. (2022). Prediction of process activities and duration based on IT ticket data: Investigating the potential of process mining and machine learning. Proceedings of the European Conference on Information Systems (ECIS 2022).
[16]. Gandla, P. K. K., Verma, R. K., Panigrahi, C. R., & Pati, B. (2024). Ticket classification using machine learning. In B. Pati, C. R. Panigrahi, P. Mohapatra, & K.-C. Li (Eds.), Proceedings of the 7th International Conference on Advance Computing and Intelligent Engineering (ICACIE 2022), Lecture Notes in Networks and Systems (Vol. 1). Springer. https://doi.org/10.1007/978-981-99-5015-7_41
[17]. Gartner. (2019). Market guide for AIOps platforms. Gartner Research.
[18]. Grohe, M. (2020). word2vec, node2vec, graph2vec, X2vec: Towards a theory of vector embeddings of structured data. Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS), 1, 16. https://doi.org/10.1145/3375395.3387641
[19]. Grover, A., & Leskovec, J. (2016). node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 855, 864. https://doi.org/10.1145/2939672.2939754
[20]. Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30, 1024, 1034.
[21]. Hitzler, P., Krötzsch, M., & Rudolph, S. (2009). Foundations of semantic web technologies. CRC Press.
[22]. Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G. de, Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), 1, 37. https://doi.org/10.1145/3447772
[23]. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. Proceedings of the International Conference on Learning Representations (ICLR 2017).
[24]. Landauer, M., Onder, S., Skopik, F., & Wurzenberger, M. (2023). Deep learning for anomaly detection in log data: A survey. Machine Learning with Applications, 12, 100470. https://doi.org/10.1016/j.mlwa.2023.100470
[25]. Lin, Q., Zhang, H., Lou, J.-G., Zhang, Y., & Chen, X. (2016). Log clustering based problem identification for online service systems. Proceedings of the 38th International Conference on Software Engineering Companion (ICSE-C), 102, 111. https://doi.org/10.1145/2889160.2889232
[26]. Meng, W., Liu, Y., Zhu, Y., Zhang, S., Pei, D., Liu, Y., Chen, Y., Zhang, R., Tao, S., Sun, P., & Zhou, R. (2019). LogAnomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19), 4739, 4745.
[27]. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26, 3111, 3119.
[28]. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529, 533. https://doi.org/10.1038/nature14236
[29]. Nacchia, M., Fruggiero, F., Lambiase, A., & Bruton, K. (2021). A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector. Applied Sciences, 11(6), 2546. https://doi.org/10.3390/app11062546
[30]. Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. (2016). A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1), 11, 33. https://doi.org/10.1109/JPROC.2015.2483592
[31]. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14, 37. https://doi.org/10.1287/orsc.5.1.14
[32]. Notaro, P., Cardoso, J., & Gerndt, M. (2021). A survey of AIOps methods for failure management. ACM Transactions on Intelligent Systems and Technology, 12(6), Article 71. https://doi.org/10.1145/3483424
[33]. Paramesh, S. P., & Shreedhara, K. S. (2019). Automated IT service desk system using machine learning techniques. Data Science Journal / CSI Transactions on ICT, 7(3), 191, 201.
[34]. Rasmussen, M. H., Lefrançois, M., Schneider, G. F., & Pauwels, P. (2020). BOT: The building topology ontology of the W3C Linked Building Data Group. Semantic Web, 12(1), 143, 161. https://doi.org/10.3233/SW-200385
[35]. Robinson, I., Webber, J., & Eifrem, E. (2015). Graph databases: New opportunities for connected data (2nd ed.). O'Reilly Media.
[36]. Salfner, F., Lenk, M., & Malek, M. (2010). A survey of online failure prediction methods. ACM Computing Surveys, 42(3), Article 10. https://doi.org/10.1145/1670679.1670680
[37]. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
[38]. Soldani, J., & Brogi, A. (2022). Anomaly detection and failure root cause analysis in (micro)service-based cloud applications: A survey. ACM Computing Surveys, 55(3), 1, 39. https://doi.org/10.1145/3501297
[39]. Sun, Z., Deng, Z.-H., Nie, J.-Y., & Tang, J. (2019). RotatE: Knowledge graph embedding by relational rotation in complex space. Proceedings of the International Conference on Learning Representations (ICLR 2019).
[40]. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
[41]. Tailhardat, L., Chabot, Y., & Troncy, R. (2023). Designing NORIA: A knowledge graph-based platform for anomaly detection and incident management in ICT systems. Proceedings of the 4th International Workshop on Knowledge Graph Construction (KGC 2023), CEUR Workshop Proceedings.
[42]. Tailhardat, L., Chabot, Y., & Troncy, R. (2023). Leveraging knowledge graphs for classifying incident situations in ICT systems. Proceedings of the 18th International Conference on Availability, Reliability and Security (ARES 2023), Article 141. https://doi.org/10.1145/3600160.3604991
[43]. Thapa, M., Espejo-Uribe, J., & Pournaras, E. (2019). Measuring network reliability and repairability against cascading failures. Journal of Intelligent Information Systems, 53(3), 511, 541. https://doi.org/10.1007/s10844-017-0477-0
[44]. van der Aalst, W. M. P. (2016). Process mining: Data science in action (2nd ed.). Springer. https://doi.org/10.1007/978-3-662-49851-4
[45]. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. Proceedings of the International Conference on Learning Representations (ICLR 2018).
[46]. Wang, H., Wu, Z., Jiang, H., Huang, Y., Wang, J., Kopru, S., & Xie, T. (2021). Groot: An event-graph-based approach for root cause analysis in industrial settings. Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021), 419, 429. https://doi.org/10.1109/ASE51524.2021.9678708
[47]. Wang, L., Zhang, C., Ding, R., Xu, Y., Chen, Q., Zou, W., Chen, Q., Zhang, M., Gao, X., Fan, H., Rajmohan, S., Lin, Q., & Zhang, D. (2023). Root cause analysis for microservice systems via hierarchical reinforcement learning from human feedback. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 5116, 5125. https://doi.org/10.1145/3580305.3599906
[48]. Watson, I., & Marir, F. (1994). Case-based reasoning: A review. The Knowledge Engineering Review, 9(4), 327, 354. https://doi.org/10.1017/S0269888900007098
[49]. Wu, L., Tordsson, J., Elmroth, E., & Kao, O. (2020). MicroRCA: Root cause localization of performance issues in microservices. Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS 2020), 1, 9. https://doi.org/10.1109/NOMS47738.2020.9110353
[50]. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4, 24. https://doi.org/10.1109/TNNLS.2020.2978386
[51]. Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How powerful are graph neural networks? Proceedings of the International Conference on Learning Representations (ICLR 2019).
[52]. Yang, B., Yih, W., He, X., Gao, J., & Deng, L. (2015). Embedding entities and relations for learning and inference in knowledge bases. Proceedings of the International Conference on Learning Representations (ICLR 2015).
[53]. Zhang, C., Peng, X., Sha, C., Zhang, K., Fu, Z., Wu, X., Lin, Q., & Zhang, D. (2022). DeepTraLog: Trace-log combined microservice anomaly detection through graph-based deep learning. Proceedings of the 44th International Conference on Software Engineering (ICSE 2022), 623, 634. https://doi.org/10.1145/3510003.3510181
[54]. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57, 81. https://doi.org/10.1016/j.aiopen.2021.01.001
[55]. Zhou, W., Xue, W., Baker, T., & Zhou, M. (2016). Resolution recommendation for event tickets in service management. IEEE Transactions on Network and Service Management, 13(4), 954, 967. https://doi.org/10.1109/TNSM.2016.2598041