Dataset opportunity
Submer — 维护日志数据集机会
Submer 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
48
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)
全球预测性维护市场在 2025 年的价值为 136.5 亿美元，预计复合年增长率为 24.30%（来源：Fortune Business Insights）。[8]
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.
Profile
Dataset profile
Type
维护日志数据集
Modality
时间序列
Sector
工业
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
部分
Legal
公司所有 — 可授权
Buyer persona
工业人工智能与维护优化供应商
Submer 拥有其工业浸没式冷却系统详细的时间序列 维护日志数据集。这些数据包括来自传感器的精细化 `iot_data` 和关于设备性能的 `industrial_data`，非常适合开发和训练预测性维护模型以预测组件故障。
全球预测性维护市场在2025 年的估值为136.5 亿美元，预计将以24.30% 的复合年增长率增长。[8] 尽管存在联合知识产权（研发数据）或需要客户同意等访问复杂性，但该数据集的稀有性及其对如此高增长市场的直接适用性，使其成为寻求在工业效率方面获得竞争优势的 AI 买家的宝贵资产。[8] ⚠ 尽职调查（有价值的数据，可协商访问）：研发数据可能受与英特尔或英伟达等芯片制造商的联合知识产权协议的约束；来自客户站点的运营数据可能需要特定的数据共享同意；流体化学和材料兼容性数据是高度专有的 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明了持有者拥有关于专业液冷环境中工业硬件性能、退化和故障的专有时间序列数据。该独特数据集直接支持预测性维护算法的开发，该市场预计将以超过 24% 的复合年增长率增长。对于工业人工智能供应商而言，这是一个难得的机会，可以获取高价值的训练数据，以构建能够预测组件故障、优化维护并为客户减少昂贵的运营停机时间的模型。
See dimension details ↓- Dataset Specificity90
主导的“维护日志”，工业领域，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 个证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式传输
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 Demand95
人工智能买家需求极高，这得益于**预测性维护**市场**24.30% 的复合年增长率**，而此类时间序列数据是其必不可少的原材料。[8]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility50
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. - 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 License92
所有权=已拥有，授权=干净
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 Orientation56
2 个数据需求信号（2 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. - ICP Audit67
⚠ 需审查 — Submer 的核心业务是销售数据中心的硬件和端到端基础设施解决方案，但它现在正扩展到提供人工智能和 GPU 即服务平台，使其成为一家技术供应商，而不是休眠数据的来源。问题：公司核心业务正演变为销售智能/计算服务；一家子公司/集团公司 Radian Arc 明确提供用于人工智能工作负载的 GPU 即服务平台。[23]；该公司现在正将自己定位为提供“端到端”
- Deep Qualification90
⚠ 需审查 — Submer 正从硬件制造商演变为全栈人工智能基础设施集团，包括人工智能即服务产品。虽然他们拥有有价值的维护和运营数据，但所有权可能与其客户混合，导致数据访问复杂，并需要协商和客户同意 [将数据/智能作为核心产品出售]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Industrial data
这些证据表明来自受控测试和与芯片制造商的联合开发的数据，提供了对特定热应力下硬件行为的深入了解。
Maintenance logs
该公司通过加速老化测试和可靠性咨询生成专有数据，直接模拟专业硬件的长期退化和故障点。
IoT / sensor data
这表明收集了来自已部署系统的真实运营数据，这些系统旨在监控和维护效率，可能来源于实时工业环境中的物联网传感器。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
JSON, CSV
License
One-time license for internal use in developing and deploying predictive maintenance models. Restrictions may apply to data redistribution or resale.
Personal data
No PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This proprietary, high-rarity time-series maintenance log dataset from industrial immersion cooling systems directly addresses the high-growth predictive maintenance market. Its granular sensor and equipment performance data are critical for developing advanced AI models, commanding a premium due to its exclusivity and direct applicability.
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Submer Maintenance Logs — a Moderate maintenance logs dataset (Time Series modality) in the industrial domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance market was valued at $13.65 billion in 2025, with a projected CAGR of 24.30% (source: Fortune Business Insights). [5]. Investment score 42.5/100 (confidence 0.49). Recommended action: Acquire.
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