Supporting knowledge exploration and discovery in multi-dimensional data with interactive multiscale visualisation

• Xiaolong Luke Zhang ^[1] ; Tim Simpson ^[2] ; Mary Frecker ^[1] ; George Lesieutre ^[1]
  1. [1] Pennsylvania State University

     Pennsylvania State University

     Borough of State College, Estados Unidos

     
  2. [2] Penn State Milton S. Hershey Medical Center

     Penn State Milton S. Hershey Medical Center

     Township of Derry, Estados Unidos

     
• Localización: Journal of Engineering Design, ISSN 0954-4828, Vol. 23, Nº. 1, 2012, págs. 23-47
• Idioma: inglés
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• Resumen

  • Knowledge discovery in multi-dimensional data is a challenging problem in engineering design. For example, in trade space exploration of large design data sets, designers need to select a subset of data of interest and examine data from different data dimensions and within data clusters at different granularities. This exploration is a process that demands both humans, who can heuristically decide what data to explore and how best to explore it, and computers, which can quickly extract features that may be of interest in the data. Thus, to support this process of knowledge discovery, we need tools that can go beyond traditional computer-oriented optimisation approaches and support advanced designer-centred trade space exploration and data interaction. This paper is an effort to address this need. In particular, we propose the interactive multiscale-nested clustering and aggregation framework to support trade space exploration of multi-dimensional data common to design optimisation. A system prototype of this framework is implemented to allow users to visually examine large design data sets through interactive data clustering, aggregation, and visualisation. The paper also presents an evaluation study involving morphing wing design using this prototype system.