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"name": "ops-lite-review-sample",
"description": "A reviewer-facing 10-case sample of the ops-lite RCA benchmark for microservice systems. Each case bundles a chaos-injection ground truth, a manifest-derived causal service graph, runtime environment snapshots, and abnormal/normal parquet telemetry tables. The sample is curated to expose both human-readable case identifiers and opaque batch-style identifiers while covering Train-Ticket (ts), Hotel Reservation from DeathStarBench (hs), and the OpenTelemetry Demo application (otel-demo).",
"url": "https://huggingface.co/datasets/anon-ops/ops-lite-review-sample",
"version": "1.0.0",
"datePublished": "2026-05-07",
"license": "https://www.apache.org/licenses/LICENSE-2.0",
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"description": "One record per curated RCA case.",
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{
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"@id": "cases_index/n_alarm_svc",
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"@id": "cases_index/primary_kind",
"name": "primary_kind",
"description": "Specific chaos type for non-hybrid cases, otherwise 'hybrid'.",
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],
"rai:dataCollection": "This repository is a reviewer-facing 10-case sample drawn from the full ops-lite benchmark. Cases are copied directly from the full release and retain the same per-case artifact structure. The sample is manually curated to remain small enough for quick inspection while still covering all three systems, multiple common fault families, and both naming styles used in the full release.",
"rai:dataCollectionType": [
"Synthetic / Simulation",
"Automated machine instrumentation"
],
"rai:dataCollectionTimeframe": {
"startDate": "2026-04-20",
"endDate": "2026-05-03"
},
"rai:dataAnnotationProtocol": "Ground-truth causal graphs are produced automatically by a manifest-driven fault-propagation reasoner (rcabench-platform v3, internal/reasoning). For each case the reasoner reads the chaos injection's fault contract from a registered fault manifest and enumerates layer-by-layer the services that the contract says can be affected, terminating when no further nodes match the contract's entry signatures. The resulting service graph is what the fault contract itself prescribes as propagation - it is not a post-filtered exhaustive walk. Cases whose resulting graph is cyclic, has longest_path <= 1, or whose injection target is a frontend / load-generator service are dropped. The kept pool is reduced to 500 by a greedy selector with hard caps on system, chaos family, and root service.",
"rai:dataAnnotationPlatform": "rcabench-platform v3 internal/reasoning module (manifest-driven layer expansion); curation script in the AegisLab repository.",
"rai:dataAnnotationAnalysis": "Annotation is fully algorithmic and deterministic given the fault manifest, so per-annotator agreement is not applicable. Quality is governed by the manifest itself: every chaos type used in this sample has an entry signature and propagation rule registered in rcabench-platform's manifest, and the graph for any case can be regenerated from the injection.json and the manifest version. Aggregate graph-shape statistics for this 10-case sample are: mean longest_path 3.50, mean n_edge 3.10, mean n_svc 3.60.",
"rai:annotationsPerItem": "1 (deterministic, manifest-derived).",
"rai:annotatorDemographics": "Not applicable - annotations are produced by software, not humans.",
"rai:machineAnnotationTools": [
"rcabench-platform v3 internal/reasoning (manifest-driven causal-graph reasoner)"
],
"rai:personalSensitiveInformation": "None. The dataset contains only synthetic load and fault telemetry from open-source microservice testbeds running in isolated lab clusters. No real user data, PII, payment data, or production traffic is present. Service / endpoint names come from the public Train-Ticket, Hotel Reservation / DeathStarBench, and OpenTelemetry Demo projects.",
"rai:dataBiases": "This sample is intentionally curated for reviewer inspection rather than statistical representativeness. Coverage is balanced across systems (4 ts, 3 hs, 3 otel-demo) and includes both named cases and opaque batch identifiers, but the sample under-represents the full benchmark's long tail of fault families and service roots. Use the full ops-lite release for benchmarking and corpus-level statistics.",
"rai:dataUseCases": "Recommended uses: (1) benchmarking microservice RCA algorithms - root-cause ranking, propagation-path inference, alarm clustering, anomaly detection on service metrics; (2) ablation studies on graph-shape difficulty (longest_path, n_edge); (3) supervision data for graph-structured RCA models. Not recommended: (1) training or evaluating production-incident triage on real customer traffic - the synthetic load and lab setting do not reflect production tail behavior; (2) general-purpose anomaly detection unrelated to microservice fault propagation; (3) any task requiring real user, employee, or business data.",
"rai:dataLimitations": "This repository is only a 10-case reviewer sample. It is suitable for spot-checking file layout, labels, and case quality, but not for quantitative evaluation or for estimating corpus-level distributions. The full benchmark remains the authoritative release for experiments.",
"rai:dataSocialImpact": "The dataset is intended to advance reproducible RCA research for microservice systems. Risks are low: there is no human or operational data. Possible indirect risk: an RCA algorithm tuned only on this benchmark may transfer poorly to production fault distributions, potentially giving operators false confidence. Users are encouraged to report any observed gaps between benchmark performance and production performance via the dataset's HF discussion page.",
"rai:dataReleaseMaintenancePlan": "Versioned releases on Hugging Face under anon-ops/ops-lite. The pipeline that produced this release (chaos injection in AegisLab + manifest-driven reasoner in rcabench-platform v3) is open-source, so any reissue can be reproduced from the same fault manifest commit. We plan to refresh the corpus when the fault manifest gains new chaos families. No deprecation timeline is set; older versions remain accessible via repo history."
} |