NDLI: Interactive visual analysis of families of curves using data aggregation and derivation

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Interactive visual analysis of families of curves using data aggregation and derivation

Content Provider	ACM Digital Library
Author	Konyha, Zoltán Lež, Alan Matković, Krešimir Hauser, Helwig Jelović, Mario
Abstract	Time-series data are regularly collected and analyzed in a wide range of domains. Multiple simulation runs or multiple measurements of the same physical quantity result in ensembles of curves which we call families of curves. The analysis of time-series data is extensively studied in mathematics, statistics, and visualization; but less research is focused on the analysis of families of curves. Interactive visual analysis in combination with a complex data model, which supports families of curves in addition to scalar parameters, represents a premium methodology for such an analysis. In this paper we describe the three levels of complexity of interactive visual analysis we identified during several case studies. The first two levels represent the current state of the art. The newly introduced third level makes extracting deeply hidden implicit information from complex data sets possible by adding data derivation and advanced interaction. We seamlessly integrate data derivation and advanced interaction into the visual exploration to facilitate an in-depth interactive visual analysis of families of curves. We illustrate the proposed approach with typical analysis patterns identified in two case studies from automotive industry.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450312424
DOI	10.1145/2362456.2362487
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-09-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Families of curves Attribute derivation Interactive visual analysis Knowledge generations
Content Type	Text
Resource Type	Article

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