RDF models and SPARQL queries for decoupled analytics demonstration

This directory contains the following: - Two sets of RDF triples using different ontologies, modeling the same "MZVAV-2" air handling unit from the data inventory by Granderson et al. [1]. - SPARQL queries for retrieving inputs to APAR [2] rules. - SPARQL queries for discovering "data links" from the models, connecting data points to time series providers. The models were created by manually writing the triples. For details, see included README.md [1] J. Granderson, G. Lin, A. Harding, P. Im, Y. Chen, Building fault detection data to aid diagnostic algorithm creation and performance testing, Scientific Data. 7 (2020) 65. https://doi.org/10.1038/s41597-020-0398-6. [2] J.M. House, H. Vaezi-Nejad, J.M. Whitcomb, An expert rule set for fault detection in air-handling units, ASHRAE Transactions. 107 (2001) 858–871.本数据集目录包含以下内容： - 两组基于不同本体（ontology）的RDF三元组，对Granderson等人[1]的数据清单中同一台“MZVAV-2”型空气处理机组进行建模。 - 用于检索APAR[2]规则输入参数的SPARQL查询。 - 用于从模型中挖掘“数据链路”、将数据点与时间序列数据源进行关联的SPARQL查询。 本数据集的模型均通过手动编写RDF三元组构建而成，详细信息请参阅附带的README.md文件。 [1] J. Granderson、G. Lin、A. Harding、P. Im、Y. Chen，《用于辅助诊断算法开发与性能测试的建筑故障检测数据集》，《科学数据》，7卷（2020年），第65页。https://doi.org/10.1038/s41597-020-0398-6. [2] J.M. House、H. Vaezi-Nejad、J.M. Whitcomb，《空气处理机组故障检测专家规则集》，《ASHRAE会刊》，107卷（2001年），第858–871页。