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From Static to Streaming:A Systematic Review and Event-Sourced Framework for GraphRAG in AIOps 认领 引用
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作者 Ferenc Erdös Vijayakumar Varadarajan +1 位作者 Viorel-Costin Banta Stephen Afrifa 《Computers, Materials & Continua》 SCIE EI 2026年第9期98-126,共29页
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... 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 softwareoperations 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 hybridfusion 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 retrievaltime governance and budget-aware traversal to help keep LLM grounding topologically accurate and compliant with incident-response Service Level Objectives(SLOs). 展开更多
关键词 Knowledge graphs retrieval-augmented generation(RAG) GraphRAG AIOps IT operations root cause analysis
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From Documents to Decisions:Enterprise-Grade LLM Systems for Zero-Hallucination,Attributed Generation,and Regulatory Alignment 认领 引用
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作者 Yenjou Wang Chihtan Cheng Jia-Wei Chang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期270-324,共55页
As large language models(LLMs)become increasingly integrated into enterprise decision-making processes,structural pressures such as version drift,cross-source evidence integration,and regulatory accountability have sh... As large language models(LLMs)become increasingly integrated into enterprise decision-making processes,structural pressures such as version drift,cross-source evidence integration,and regulatory accountability have shifted the primary challenge from isolated generative performance to system-level consistency,traceability,and governability.This paper systematically reviews key technological developments relevant to enterprise requirements,including document perception,retrieval-augmented generation(RAG),hybrid RAG-KG architectures,fine-grained attribution evaluation,and multi-agent coordination.The analysis demonstrates that the main obstacle to enterprise LLM adoption is not model capability,but rather the structural gap between fragmented technical modules and the need for high-reliability decision-making.In response,a risk-controlled data flywheel architecture is proposed that integrates perception,reasoning,verification,and governance layers.By converting reasoning outputs into observable risk signals and feeding them back into retrieval and structural components,this architecture establishes a continuous improvement loop.This approach provides a systematic deployment blueprint for enterprise-grade LLM systems,emphasizing traceability,accountability,and sustainable optimization in high-risk and long-term operational contexts. 展开更多
关键词 Large language models(LLMs) retrieval-augmented generation(RAG) knowledge graph(KG) optical character recognition(OCR) enterprise AI systems risk-controlled architecture governance and compliance attribution and faithfulness multi-agent systems data flywheel
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