Shape Expressions for Specifying and Extracting Signal Features
Shape Expressions for Specifying and Extracting Signal Features
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DOI：
10.1007/978-3-030-32079-9_17
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发表时间：
2019-10
影响因子：
0.8
通讯作者：
D. Ničković;Xin Qin;Thomas Ferrère;Cristinel Mateis;Jyotirmoy V. Deshmukh
中科院分区：
文献类型：
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作者：
D. Ničković;Xin Qin;Thomas Ferrère;Cristinel Mateis;Jyotirmoy V. Deshmukh
网络物理系统（CPS）和物联网（IoT）产生了大量生成、测量和记录的时间序列数据。从这些大量的数据中提取编码模式的时间段是一个非常困难的问题。我们proposeshape expressions作为一个声明性的形式主义，用于指定，查询和提取复杂的时间模式，从可能的噪音数据。形状表达式是具有任意（线性、指数、正弦等）具有作为原子谓词的参数和对这些参数的附加约束的形状。我们配备了形状表达式与novelnoisysemantics相结合的正则表达式匹配语义与统计回归。我们描述的形式主义的基本属性，并提出了一个有效的近似形状表达式匹配过程。我们证明了广泛的适用性，这种技术的两个案例研究。
Cyber-physical systems (CPS) and the Internet-of-Things (IoT) result in a tremendous amount of generated, measured and recorded time-series data. Extracting temporal segments that encode patterns with useful information out of these huge amounts of data is an extremely difficult problem. We proposeshape expressionsas a declarative formalism for specifying, querying and extracting sophisticated temporal patterns from possibly noisy data. Shape expressions are regular expressions with arbitrary (linear, exponential, sinusoidal, etc.) shapes with parameters as atomic predicates and additional constraints on these parameters. We equip shape expressions with a novelnoisysemantics that combines regular expression matching semantics with statistical regression. We characterize essential properties of the formalism and propose an efficient approximate shape expression matching procedure. We demonstrate the wide applicability of this technique on two case studies.