Mining Interpretable Spatio-temporal Logic Properties for Spatially Distributed Systems
Mining Interpretable Spatio-temporal Logic Properties for Spatially Distributed Systems
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DOI：
10.1007/978-3-030-88885-5_7
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发表时间：
2021-06
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影响因子：
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通讯作者：
Sara Mohammadinejad;Jyotirmy V. Deshmukh;L. Nenzi
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文献类型：
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作者：
Sara Mohammadinejad;Jyotirmy V. Deshmukh;L. Nenzi
物联网、复杂的传感器网络、多智能体网络物理系统都是随时间不断演化的空间分布式系统的例子。这样的系统会产生大量的时空数据，系统设计人员通常对分析和发现数据中的结构感兴趣。有相当大的兴趣在学习因果和逻辑属性的时间数据使用逻辑，如信号时序逻辑（STL），但是，有有限的工作发现这种关系onspatio-temporaldata。我们提出了第一套时空数据无监督学习算法。我们的方法做自动特征提取的时空数据投影到参数空间的parametric时空到达和逃逸逻辑（PSTREL）。我们提出了一个凝聚层次聚类技术，保证每个集群满足一个不同的STREL公式。我们表明，我们的方法产生STREL公式的描述复杂性有界的使用一种新的决策树方法，概括了以前的无监督学习技术的信号时序逻辑。我们证明了我们的方法的有效性，从不同领域的案例研究，如城市交通，流行病学，绿色基础设施，空气质量监测。
The Internet-of-Things, complex sensor networks, multi-agent cyber-physical systems are all examples of spatially distributed systems that continuously evolve in time. Such systems generate huge amounts of spatio-temporal data, and system designers are often interested in analyzing and discovering structure within the data. There has been considerable interest in learning causal and logical properties of temporal data using logics such as Signal Temporal Logic (STL); however, there is limited work on discovering such relations onspatio-temporaldata. We propose the first set of algorithms forunsupervised learningfor spatio-temporal data. Our method does automatic feature extraction from the spatio-temporal data by projecting it onto the parameter space of aparametric spatio-temporal reach and escape logic(PSTREL). We propose an agglomerative hierarchical clustering technique that guarantees that each cluster satisfies a distinct STREL formula. We show that our method generates STREL formulas of bounded description complexity using a novel decision-tree approach which generalizes previous unsupervised learning techniques for Signal Temporal Logic. We demonstrate the effectiveness of our approach on case studies from diverse domains such as urban transportation, epidemiology, green infrastructure, and air quality monitoring.