EnviroStream: A Stream Reasoning Benchmark for Climate and Ambient Monitoring

Stream Reasoning (SR) focuses on developing advanced approaches for applying inference to dynamic data streams; it has become increasingly relevant in various application scenarios such as IoT, Smart Cities, Emergency Management, and Healthcare, despite being a relatively new field of research. The current lack of standardized formalisms and benchmarks has been hindering the comparison between different SR approaches. <br> We propose a new benchmark, called <em>EnviroStream</em>, for evaluating SR systems on weather and environmental data from two European cities. The benchmark includes queries and datasets of different sizes. We adopt <em>I-DLV-sr</em>, a recently released SR system based on Answer Set Programming, as a baseline experiment. We illustrate how the queries can be modeled via <em>I-DLV-sr</em> input language and report evaluation times. We also assess continuous online reasoning via a web application. ############################################################################################ Data can and queries can be also downloaded via the GitHub repository: https://github.com/DeMaCS-UNICAL/EnviroStream Real-time data can be visualized via the following link: https://experiments.demacs.unical.it/流推理（Stream Reasoning，SR）致力于研发面向动态数据流的高级推理方法；尽管作为一个相对新兴的研究领域，其在物联网（Internet of Things, IoT）、智慧城市、应急管理以及医疗健康等诸多应用场景中的重要性与日俱增。当前流推理领域缺乏标准化的形式化体系与评测基准，这阻碍了不同流推理方法之间的性能对比。我们提出了一款名为EnviroStream的新型评测基准，用于基于两座欧洲城市的气象与环境数据对流推理系统进行性能评测。该基准涵盖不同规模的查询任务与数据集。我们采用基于回答集编程（Answer Set Programming）的新近发布流推理系统I-DLV-sr作为基准实验对照模型。我们演示了如何通过I-DLV-sr的输入语言对查询任务进行建模，并报告了各项评测的耗时情况。此外，我们还通过一款Web应用程序对连续在线推理任务进行了性能评估。 ############################################################################################ 数据集与查询任务均可通过该GitHub仓库下载：https://github.com/DeMaCS-UNICAL/EnviroStream；实时数据可通过以下链接进行可视化展示：https://experiments.demacs.unical.it/