Change-Induced Incident Causal Diagnosis Log Dataset

#causal diagnosis #change-to-incident correlation analysis #operational log evidence analysis #IT incident management #change impact analysis #service failure troubleshooting

4200
0.3GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-05

AI Analysis & Value Prop

This dataset brings together ITSM change records and related deployment, configuration, service, and incident logs, with supervised labels for incident outcomes, the change's causal role, potential failure mechanisms, and supporting evidence. Causal chains, temporal assessments, supporting or contradicting observations, alternative explanations, and diagnostic rationales help models distinguish causal evidence from mere temporal proximity. It supports IT operations troubleshooting, change-impact reviews, and the training and evaluation of causal-reasoning models.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
evidence	array	Specific observations from the raw records that support, contradict, or contextualize the causal diagnosis.
diagnosis	object	The supervised assessment of the incident outcome, the change's causal role, confidence, and the proposed failure mechanism.
causal_chain	array	Ordered events that explain the proposed path from the change to the service outcome. Include only evidence-supported links and identify uncertain links in their descriptions.
change_record_raw	string	The original ITSM change record serialized as JSON, including its identifier, scope, planned timing, implementation details, and status when available.
temporal_assessment	object	An assessment of event ordering and timing. Timing can establish consistency with a hypothesis but does not by itself establish causality.
diagnostic_rationale	string	A concise reasoning summary connecting the evidence, causal mechanism, temporal assessment, and alternatives to the final attribution. Do not infer causality from temporal proximity alone.
operational_logs_raw	string	Original deployment, configuration, service, and incident events serialized as JSON Lines, preserving timestamps and observed states when available.
recommended_validation	array	Checks that could confirm or refute the diagnosis, such as comparing pre- and post-change state, reviewing correlated telemetry, or reproducing the behavior.
alternative_explanations	array	Plausible non-change causes or competing explanations considered, with a concise account of whether the available evidence supports or weakens each one.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?: It includes raw ITSM change records and deployment, configuration, service, or incident logs, together with annotations such as causal diagnoses, causal chains, temporal assessments, supporting evidence, alternative explanations, and recommended validation checks.

How does it avoid treating timing alone as proof of causation?: The annotations assess event states, log evidence, plausible failure mechanisms, and alternative explanations. Event order can show whether a hypothesis is temporally consistent, but it does not by itself prove that a change caused an incident. Uncertain links in a proposed causal chain should also be identified.

What are the data formats and quality characteristics?: Raw change records are represented as JSON, while operational logs are represented as JSON Lines. Supervised fields cover the diagnosis and its evidence and reasoning. Timestamps and observed states are preserved when available, so field completeness may vary across records.

How can I obtain the data, and what are the license terms?: Refer to the product page for the available download or delivery method, and review the applicable license before use. Commercial use, redistribution, and production training are subject to the specific terms provided with the dataset.

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Cite this Work

@dataset{Mobiusi2026,
  title={Change-Induced Incident Causal Diagnosis Log Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/9448a1fd691738da9c38dc247c659b98},
  urldate={2026-10-22},
  keywords={change-induced incident,ITSM change logs,operational incident diagnosis,failure causal analysis,change impact assessment,log-based causal reasoning,reasoning SFT dataset,service failure attribution},
  version={1.0}
}

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