Extracting Knowledge from Sensor Signals for Case-Based Reasoning with Longitudinal Time Series Data
Extracting Knowledge from Sensor Signals for Case-Based Reasoning with Longitudinal Time Series Data
复制标题
从传感器信号中提取知识，利用纵向时间序列数据进行基于案例的推理
DOI：
10.1007/978-3-540-73180-1_9
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
N. Xiong
中科院分区：
文献类型：
--
作者：
P. Funk;N. Xiong
在许多工业和医疗诊断问题中，有必要研究收集的时间序列测量值以识别现有或潜在的故障/疾病。如今，这通常由人类手动完成。然而，实践中信号的冗长和复杂性往往使得即使是经验丰富的专家正确分析和解释可用数据也是一项乏味而艰巨的任务。基于案例推理等智能数据分析方法的结合在为技术人员和临床医生提供决策支持方面显示出强大的优势，以实现更可靠和更有效的判断。本章讨论了一个通用框架，能够更紧凑和有效地表示实际时间序列案例，捕获最重要的特征，同时忽略不相关的琐碎细节。我们的目标是从原始的、通常是实值的时间序列数据中提取一组定性的、可解释的特征。这些功能一方面应向人类专家传达重要信息，从而实现潜在的发现/发现，另一方面应在基于案例的推理中促进大大简化的案例索引和相似性匹配。实现这一目标的路线图包括两个后续阶段。在第一阶段，其任务是通过时间抽象或符号近似将实数时间序列转换为符号序列。此阶段有几种不同的方法可用，本章将介绍它们。然后在第二阶段，我们使用知识发现方法从转换后的符号序列中根据它们与某些类的共现来识别关键序列。这些关键序列对于提供简洁且重要的特征来表征原始时间序列信号的动态特性非常有价值。本章讨论了使用发现的键序列对时间序列案例进行索引的四种替代方法。
In many industrial and medical diagnosis problems it is essential to investigate time series measurements collected to recognize existing or potential faults/diseases. Today this is usually done manually by humans. However the lengthy and complex nature of signals in practice often makes it a tedious and hard task to analyze and interpret available data properly even by experts with rich experiences. The incorporation of intelligent data analysis method such as case-based reasoning is showing strong benefit in offering decision support to technicians and clinicians for more reliable and efficient judgments.This chapter addresses a general framework enabling more compact and efficient representation of practical time series cases capturing the most important characteristics while ignoring irrelevant trivialities. Our aim is to extract a set of qualitative, interpretable features from original, and usually real-valued time series data. These features should on one hand convey significant information to human experts enabling potential discoveries/findings and on the other hand facilitate much simplified case indexing and similarity matching in case-based reasoning. The road map to achieve this goal consists of two subsequent stages. In the first stage it is tasked to transform the time series of real numbers into a symbolic series by temporal abstraction or symbolic approximation. A few different methods are available at this stage and they are introduced in this chapter. Then in the second stage we use knowledge discovery method to identify key sequences from the transformed symbolic series in terms of their cooccurrences with certain classes. Such key sequences are valuable in providing concise and important features to characterize dynamic properties of the original time series signals. Four alternative ways to index time series cases using discovered key sequences are discussed in this chapter.