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Open Access

REVIEW

From Static to Streaming: A Systematic Review and Event-Sourced Framework for GraphRAG in AIOps

Ferenc Erdős1,*, Vijayakumar Varadarajan2,3,4, Viorel-Costin Banţa5, Stephen Afrifa6,7
1 Department of Informatics, Faculty of Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary
2 Swiss School of Business and Management Geneva, Geneva, Switzerland
3 Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Semarang, Indonesia
4 Faculty of Technical and STEAM, HANO Technical University, Mogadishu, Somalia
5 Department of Management Information Systems, Bucharest University of Economic Studies, Bucharest, Romania
6 Artificial Intelligence and Robotics Laboratory, North Carolina State University, Raleigh, NC, USA
7 School of Electrical and Information Engineering, Tianjin University, Tianjin, China
* Corresponding Author: Ferenc Erdős. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.081005

Received 21 February 2026; Accepted 20 May 2026; Published online 15 June 2026

Abstract

Standard retrieval-augmented generation (RAG) can perform poorly in AI for IT Operations (AIOps) settings because it is topology-blind. Basic RAG retrieves isolated, flat text snippets without enforcing structural or causal constraints, causing large language models to generate explanations that contradict the running system’s actual dependency structure. To address this gap, we conducted a systematic review following PRISMA 2020, searching Scopus, IEEE Xplore, Web of Science, and Google Scholar (last searched 31 January 2026). We included empirical or systems-oriented studies applying graph-based retrieval to ground a generative model in an IT, cloud, or software-operations setting, and excluded generic RAG without an operational context and graph-only methods without a generative component. Of 139 unique records, 31 met the criteria. Because reported metrics, tasks, and hardware were too heterogeneous for pooled effect estimates, we performed a descriptive quantitative synthesis of reporting frequencies for five outcome variables (localization accuracy, text/classification scores, MTTR, retrieval/inference latency, and graph construction cost), with values harmonized to common units. The synthesis reveals that hybrid-fusion approaches have become the dominant retrieval strategy, outpacing standalone traversal in adoption for Root Cause Analysis (RCA) tasks by fusing semantic vector search with strict structural constraints. However, our evaluation matrix exposes a critical production barrier: in 73% of Service Dependency Graph (SDG)-centric studies, topology drift or streaming-update handling is not explicitly described, with many pipelines evaluated on static or periodically refreshed snapshots. We outline Event-Sourced Streaming GraphRAG (ES-GraphRAG) as a reference architecture that frames concrete design requirements for latency and drift constraints based on an event-sourced, streaming construction pattern for snapshot-consistent retrieval. The framework also incorporates strict retrieval-time governance and budget-aware traversal to help keep LLM grounding topologically accurate and compliant with incident-response Service Level Objectives (SLOs).

Keywords

Knowledge graphs; retrieval-augmented generation (RAG); GraphRAG; AIOps; IT operations; root cause analysis

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