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2016•Unpublished venueRequires access

Visual Analytics of Relations of Multi-Attributes in Big Infrastructure Data

Jianlong Zhou, Zelin Li, Zongjian Zhang, Bin Liang, Fang Chen

Open publisher page 7 citations

Abstract

This paper presents information visualization methods for revealing relations of multi-attributes in big infrastructure data. The interactive parallel coordinates, sunburst visualization and combinational visualization approaches are used to represent different relations to get insights from the big infrastructure data. The water pipe failure data is used as a case study to show the effectiveness of proposed visual analytics approaches.

About this research paper

What this paper is about

This paper presents information visualization methods for revealing relations of multi-attributes in big infrastructure data. The interactive parallel coordinates, sunburst visualization and combinational visualization approaches are used to represent different relations to get insights from the big infrastructure data. The water pipe failure data is used as a case study to show the effectiveness of proposed visual analytics approaches.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

This paper presents information visualization methods for revealing relations of multi-attributes in big infrastructure data. The interactive parallel coordinates, sunburst visualization and combinational visualization approaches are used to represent different relations to get insights from the big infrastructure data. The water pipe failure data is used as a case study to show the effectiveness of proposed visual analytics approaches.

Key concepts: Visual analytics, Visualization, Big data, Interactive visual analysis, Computer science, Data visualization, Cultural analytics, Data science

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