A Work-Centered Visual Analytics Model to Support Engineering Design with Interactive Visualization and Data-Mining

To support the knowledge discovery and decision making from large-scale, multi-dimensional, continuous data sets, novel systems of visual analytics need the capability to identify hidden patterns in data that are critical for in-depth analysis. In this paper, we present a work-centered approach to s...

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Bibliographic Details
Published in:2012 45th Hawaii International Conference on System Sciences pp. 1845 - 1854
Main Authors: Xin Yan, Mu Qiao, Jia Li, Simpson, Timothy W., Stump, Gary M., Xiaolong Zhang
Format: Conference Proceeding
Language:English
Published: IEEE 01.01.2012
Subjects:
Algorithm design and analysis
Clustering algorithms
Computational modeling
Data mining
Data models
Data visualization
Visual analytics
ISBN:9781457719257, 1457719258
ISSN:1530-1605, 1530-1605
Online Access:Get full text
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Summary:To support the knowledge discovery and decision making from large-scale, multi-dimensional, continuous data sets, novel systems of visual analytics need the capability to identify hidden patterns in data that are critical for in-depth analysis. In this paper, we present a work-centered approach to support visual analytics of complex data sets by combining user-centered interactive visualization and data-oriented computational algorithms. We design and implement a specific system prototype, Learning-based Interactive Visualization for Engineering design (LIVE), for engineering designers to handle overwhelming information such as numerous design alternatives generated from automatic simulating software. During the exploration within a "trade space" consisting of possible designs and potential solutions, engineering designers want to analyze the data, discover hidden patterns, and identify preferable solutions. The proposed system allows designers to interactively examine large design data sets through visualization and interactively construct data models from automatic data mining algorithms. We expect that our approach can help designers efficiently and effectively make sense of large-scale design data sets and generate decisions. We also report a preliminary evaluation on our system by analyzing a real engineering design problem related to aircraft wing sizing.
ISBN:9781457719257
1457719258
ISSN:1530-1605
1530-1605
DOI:10.1109/HICSS.2012.87