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arXiv 2403.14701cs.CYcs.DB
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智慧城市空气质量监测系统中基于规则的复杂事件处理

Rule based Complex Event Processing for an Air Quality Monitoring System in Smart City

Shashi Shekhar Kumar, Ritesh Chandra, Sonali Agarwal

AI总结:

本研究提出一个集成框架,利用基于规则的复杂事件处理和 SPARQL 查询实时监测智慧城市空气质量,通过知识图谱和决策树规则提取增强利益相关者的决策支持。

摘要:

近年来,基于智慧城市的发展因其架构和规划在人类系统居住方面的多功能性而获得了动力。根据世界卫生组织(WHO)的报告,空气污染会导致严重的呼吸系统疾病。因此,必须实时监测空气质量,以便利益相关者做出限时决策来减轻影响。空气污染包含 NH3、O3、SO2、NO2 等多种成分,其浓度因地点而异。本研究提出了一个集成框架,使用基于规则的复杂事件处理(CEP)和 SPARQL 查询来监测空气质量。CEP 根据预定义规则处理数据流以检测复杂模式,为利益相关者提供决策支持。最初,数据集收集自印度中央污染控制委员会(CPCB),随后进行预处理并通过 Apache Kafka 传递。接着基于空气质量范式开发了知识图谱。随后,将预处理数据转换为 Resource Description Framework(RDF)数据,并与知识图谱集成,使用 Apache Jena 将其摄入 CEP 引擎以增强决策支持。同时,使用决策树提取规则,并添加 CPCB 的一些基准参数,将其摄入 CEP 引擎以确定复杂模式。随后,在实时 RDF 数据集上使用 SPARQL 查询,基于复杂事件检测获取空气质量状况(如良好、差、严重、危险等)。为验证所提方法,使用多个 RDF 数据块将事件部署到 CEP 引擎,并在执行简单和复杂查询时检查其随时间变化的性能。

英文摘要:

In recent years, smart city-based development has gained momentum due to its versatile nature in architecture and planning for the systematic habitation of human beings. According to World Health Organization (WHO) report, air pollution causes serious respiratory diseases. Hence, it becomes necessary to real-time monitoring of air quality to minimize effect by taking time-bound decisions by the stakeholders. The air pollution comprises various compositions such as NH3, O3, SO2, NO2, etc., and their concentrations vary from location to location.The research work proposes an integrated framework for monitoring air quality using rule-based Complex Event Processing (CEP) and SPARQL queries. CEP works with the data stream based on predefined rules to detect the complex pattern, which helps in decision support for stakeholders. Initially, the dataset was collected from the Central Pollution Control Board (CPCB) of India and this data was then preprocessed and passed through Apache Kafka. Then a knowledge graph developed based on the air quality paradigm. Consequently, convert preprocessed data into Resource Description Framework (RDF) data, and integrate with Knowledge graph which is ingested to CEP engine using Apache Jena for enhancing the decision support . Simultaneously, rules are extracted using a decision tree, and some ground truth parameters of CPCB are added and ingested to the CEP engine to determine the complex patterns. Consequently, the SPARQL query is used on real-time RDF dataset for fetching the condition of air quality as good, poor, severe, hazardous etc based on complex events detection. For validating the proposed approach various chunks of RDF are used for the deployment of events to the CEP engine, and its performance is examined over time while performing simple and complex queries.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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