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Extracting knowledge from sensor signals for case-based reasoning with longitudinal time series data
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0002-5562-1424
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0001-9857-4317
2008 (English)In: Case-Based Reasoning in Signals and Images / [ed] Petra Perner, Springer, 2008, p. 247-284Chapter in book (Other academic)
Abstract [en]

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 imilarity 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.

Place, publisher, year, edition, pages
Springer, 2008. p. 247-284
Series
Studies in Computational Intelligence ; 73
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-6979DOI: 10.1007/978-3-540-73180-1_9Scopus ID: 2-s2.0-42449133805ISBN: 978-3-540-73178-8 (print)OAI: oai:DiVA.org:mdh-6979DiVA, id: diva2:236989
Available from: 2009-09-25 Created: 2009-09-25 Last updated: 2016-05-17Bibliographically approved

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Funk, PeterXiong, Ning

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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  • de-DE
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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