Due to intelligent, adaptive nature towards various operations and their ability to provide maximum comfort to the occupants residing in them, smart buildings are becoming a pioneering area of research. Since these architectures leverage the Internet of Things (IoT), there is a need for monitoring different operations (Occupancy, Humidity, Temperature, CO2, etc.) to provide sustainable comfort to the occupants. This paper proposes a novel approach for intelligent building operations monitoring using rule-based complex event processing and query-based approaches for dynamically monitoring the different operations. Siddhi is a complex event processing engine designed for handling multiple sources of event data in real time and processing it according to predefined rules using a decision tree. Since streaming data is dynamic in nature, to keep track of different operations, we have converted the IoT data into an RDF dataset. The RDF dataset is ingested to Apache Kafka for streaming purposes and for stored data we have used the GraphDB tool that extracts information with the help of SPARQL query. Consequently, the proposed approach is also evaluated by deploying the large number of events through the Siddhi CEP engine and how efficiently they are processed in terms of time. Apart from that, a risk estimation scenario is also designed to generate alerts for end users in case any of the smart building operations need immediate attention. The output is visualized and monitored for the end user through a tableau dashboard.

翻译：由于智能建筑对各种操作具有自适应性，并能最大程度地提升居住者的舒适度，其已成为一个前沿研究领域。这些架构利用物联网技术，因此需要监控不同运行参数（如人员占用率、湿度、温度、CO₂浓度等），以可持续地保障居住者舒适度。本文提出了一种基于规则的复杂事件处理与查询驱动的智能建筑运行监控新方法，用于动态监测不同运行状态。Siddhi是一种复杂事件处理引擎，能够实时处理多源事件数据，并根据预定义规则通过决策树进行数据处理。考虑到流数据具有动态特性，为追踪不同运行状态，我们将物联网数据转换为RDF数据集。该RDF数据集被摄入Apache Kafka以实现流式处理，对于已存储数据则采用GraphDB工具通过SPARQL查询提取信息。此外，我们通过向Siddhi CEP引擎部署大量事件来评估所提方法的处理时效性。同时，设计风险预警场景，在智能建筑任何运行状态需要紧急干预时向用户生成警报。最终结果通过Tableau仪表盘进行可视化监控。