Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper
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Abstract (arXiv)
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between the research areas of machine learning, big data, streaming analytics, and the management of IT operations. AIOps, as a field, is a candidate to produce the future standard for IT operation management. To that end, AIOps has several challenges. First, it needs to combine separate research branches from other research fields like software reliability engineering. Second, novel modelling techniques are needed to understand the dynamics of different systems. Furthermore, it requires to lay out the basis for assessing: time horizons and uncertainty for imminent SLA violations, the early detection of emerging problems, autonomous remediation, decision making, support of various optimization objectives. Moreover, a good understanding and interpretability of these aiding models are important for building trust between the employed tools and the domain experts. Finally, all this will result in faster adoption of AIOps, further increase the interest in this research field and contribute to bridging the gap towards fully-autonomous operating IT systems. The main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field. The workshop aims to strengthen the community and unite it towards the goal of joining the efforts for solving the main challenges the field is currently facing. A consensus and adoption of the principles of openness and reproducibility will boost the research in this emerging area significantly.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 15 Jan 2021.
Builds on
6 of 28 references.
References · 6
- 2020Self-Attentive Classification-Based Anomaly Detection in Unstructured Logscited by 36
- 2020Self-Supervised Log Parsingcited by 23
- 2020Anomaly Detection at Scale: The Case for Deep Distributional Time Series Modelscited by 7
- 2020A Systematic Mapping Study in AIOpscited by 7
- 2021Multi-Source Anomaly Detection in Distributed IT Systemscited by 4
- 2020Towards Runtime Verification via Event Stream Processing in Cloud Computing Infrastructurescited by 1
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1; 2025: 1; 2026: 1.
7 papers.
- 2021Online false discovery rate control for anomaly detection in time seriescited by 13
- 2026NetInjectBench: Benchmarking Indirect Prompt Injection in Tool-Using Large Language Model Agents for Network Operationscited by 4
- 2024AIOps Solutions for Incident Management: Technical Guidelines and A Comprehensive Literature Reviewcited by 3
- 2022Intrinsic Anomaly Detection for Multi-Variate Time Seriescited by 2
- 2025SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logscited by 2
- 2023On-Premise AIOps Infrastructure for a Software Editor SME: An Experience Reportcited by 1
- 2023Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targetscited by 1
- 2026NetInjectBench: Benchmarking Indirect Prompt Injection in Tool-Using Large Language Model Agents for Network Operations11 Jul 2026
- 2025SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs20 May 2025
- 2024AIOps Solutions for Incident Management: Technical Guidelines and A Comprehensive Literature Review1 Apr 2024
- 2023Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets1 Oct 2023
- 2023On-Premise AIOps Infrastructure for a Software Editor SME: An Experience Report22 Aug 2023
- 2022Intrinsic Anomaly Detection for Multi-Variate Time Series29 Jun 2022
- 2021Online false discovery rate control for anomaly detection in time series6 Dec 2021