发表机构
Georgia Institute of Technology; Embry-Riddle Aeronautical University（佐治亚理工学院; 埃姆布里-里德尔航空大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SensorWF提出一个FAIR标注的五模块工作流框架，通过领域适配器隔离领域逻辑，实现跨航天、医疗、气候等传感器领域的通用时间序列分析，并支持溯源与本体输出。
AI 中文摘要
科学传感器数据是航天工程、临床医学和大气科学等学科的基础。在每种情境中，都会构建管道来摄取原始档案、评估数据质量、执行特征工程和语义标注，并记录溯源信息。然而，这些管道通常以单体式、特定领域的脚本实现，带有隐含假设，跨领域复用性有限。本工作介绍了SensorWF，一个用于通用科学时间序列分析的FAIR标注工作流框架。该框架具有一个五模块可复用核心（M1-M5），带有类型化输入/输出契约。领域适配器模式将所有领域特定逻辑隔离在M1中，使M2-M5模块能够跨学科以相同方式运行。领域假设被编码在机器可读的组件注册表中，从而无需修改分析核心即可跨传感器领域复用。SensorWF还为所有文件路径实体生成带有SHA-256校验和的运行时PROV-O/ProvONE溯源轨迹，并输出与SSN/SOSA对齐的OWL本体作为主要输出。为评估通用性，SensorWF在三个不同科学领域进行了实例化：航天器遥测、动态心电图和大气气候，并展示了合成故障注入和多检测器异常检测作为用例扩展。结果表明，仅通过每个约170-500行的M1适配器进行参数化的单一代码库，即可支持跨不同采样率、通道数和故障分类法的领域分析管道。所有代码、组件注册表、本体工件和数据集均作为开放科学对象提供。我们的代码库可在此https URL公开获取。
英文摘要
Scientific sensor data is foundational across disciplines including spacecraft engineering, clinical medicine, and atmospheric science. In each context, pipelines are constructed to ingest raw archives, assess data quality, perform feature engineering and semantic annotation, and record provenance. However, these pipelines are often implemented as monolithic, domain-specific scripts with implicit assumptions and limited reusability across fields. This work introduces SensorWF, a FAIR-annotated workflow framework for generalizable scientific time-series analysis. The framework features a five-module reusable core (M1-M5) with typed input/output contracts. A domain adapter pattern isolates all domain-specific logic within M1, enabling modules M2-M5 to operate identically across disciplines. Domain assumptions are encoded in a machine-readable component registry, enabling reuse across sensor domains without modifying the analytical core. SensorWF also generates runtime PROV-O/ProvONE provenance traces with SHA-256 checksums for all file-path entities and emits SSN/SOSA-aligned OWL ontologies as primary outputs. To assess generalizability, SensorWF is instantiated in three distinct scientific domains: spacecraft telemetry, ambulatory ECG, and atmospheric climate, with synthetic fault injection and multi-detector anomaly detection demonstrated as use-case extensions. Results show that a single codebase, parameterized solely through M1 adapters of approximately 170-500 lines each, supports analytical pipelines across domains with varying sampling rates, channel counts, and fault taxonomies. All code, the component registry, ontology artifacts, and datasets are made available as an open scientific object. Our codebase is publicly available at https://purl.archive.org/sensor-wf.
Comments10 pages, 4 figures, 4 tables. Accepted for publication in the Proceedings of the ReWorDS26 Workshop, 22nd IEEE International Conference on eScience (eScience 2026)
