Dataset opportunity
Caerdav — 维护日志数据集机会
Caerdav 持有的中等维护日志数据集，可用于预测性维护和异常检测。
Score
68.9
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
收购
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
全球飞机预测性维护市场在 2025 年的估值为 45.1 亿美元，预计在 2026-2034 年期间的复合年增长率为 17.1%。
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
- ✨Signal
利用技术专长的航空咨询服务
source ↗
Profile
Dataset profile
Type
维护日志数据集
Modality
时间序列
Sector
mobility
Volume
中等
Freshness
定期
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — 许可权待澄清
Buyer persona
工业人工智能与维护优化供应商
Caerdav 持有一个结构为时间序列的详细维护日志数据集。该数据集包含细粒度的 `inspection_records`（检查记录）和历史 `maintenance_logs`（维护日志），提供了组件健康状况和干预措施的全面时间线。这种格式非常适合开发和训练预测性维护算法，以准确预测组件故障并优化 MRO（维护、修理和大修）计划。
商业价值锚定在全球航空预测性维护市场，该市场在 2025 年的估值为45.1 亿美元，预计将以 17.1% 的复合年增长率增长。[3] 虽然由于与租赁商的共同所有权、严格的 CAA/EASA 监管要求以及孤立的遗留数据，访问权限复杂，但这些障碍使得经过精心策划、可供分析的数据具有非凡的价值。克服这种复杂性可以带来显著的竞争优势，因为数据本身稀有且难以汇总。⚠ 尽职调查（有价值的数据，可协商的访问权限）：维护记录通常与飞机租赁商和航空公司共同拥有或合同绑定；严格的航空监管要求（CAA/EASA）关于数据完整性和技术记录；数据可能孤立在物理纸质日志或遗留 MRO 管理软件中。· 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据证实 Caerdav 持有高需求商用飞机（包括波音 737 和空客 A320 系列）的专有时间序列 维护日志。该数据集是工业人工智能供应商构建和验证预测性维护算法的关键资产。在一个年增长率预计超过 17% 的市场中，这些稀有数据提供了优化飞机适航状态和抢占可观市场份额所需的地面实况。
See dimension details ↓- Acquisition Feasibility30
中等难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 种证据类型，3 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License36
所有权=混合，许可=权利不明确
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 个数据需求信号（1 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高 — 专有数据超出已货币化的部分
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - Dataset Specificity90
主导的“maintenance_logs”，行业 mobility，3 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 次证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness46
定期
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适用于预测性维护
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
人工智能买家需求异常高，这得益于市场强劲的预计增长（17.1% 的复合年增长率），因为航空公司正积极采用数据驱动策略来提高安全性和运营效率。[3]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
受限/未知
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - ICP Audit100
✓ 良好目标 — Caerdav 是一个极好的目标，作为一家独立的飞机 MRO 中小型企业，其核心业务是实体维护，这会产生有价值的维护日志数据作为副产品，并且似乎不进行销售。问题：该公司使用第三方人工智能/软件（KeepFlying 的 FinTwin 和 ELMS Aviation 的平台）来提高自身运营效率，而不是出售情报。
- Deep Qualification30
⚠ 需要审查 — Caerdav 作为其核心 MRO 服务的副产品生成指定的维护日志数据。然而，航空法规（EASA M.A.307）强烈表明，这些技术记录是飞机所有者/运营商（其客户）的合法财产，因此 Caerdav 极不可能拥有出售这些数据的权利。[数据归公司客户所有；许可受限]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Maintenance logs
这些证据指向高销量商用飞机的认证基地维护记录，提供了训练预测性故障模型所需的基本时间序列数据。
Inspection reports
这些是重大干预措施（如发动机更换和改装）的详细记录，为丰富预测模型和理解完整的飞机生命周期提供了关键背景。
Industrial data
这些数据捕获了使停飞飞机保持适航状态所需的特定维护活动，这是专注于优化地面运行和周转时间的模型的宝贵输入。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Caerdav Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global predictive airplane maintenance market was valued at $4.51 billion in 2025, projected to grow at a CAGR of 17.1% (2026-2034) (source: Fortune Business Insights). [3]. Investment score 68.9/100 (confidence 0.49). Recommended action: Acquire.
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