Enhancing statistical charts: toward better data visualization and analysis

Luo, Xiangfeng; Yuan, Yuan; Zhang, Kaiyuan; Xia, Jiazhi; Zhou, Zhiguang; Chang, Liang; Gu, Tao

doi:10.1007/s12650-019-00569-2

Cited by 9 publications

(1 citation statement)

References 106 publications

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“…Manufacturing enterprises generate large number of statistical data on product research, production quality, sales, after-sales, etc., during the production process. Most of them are directly saved as chart in that the vivid graphic structure can intuitively and effectively reflect the relationship and characteristics of the data [9]. The information of chart can be used by decision makers to understand the full life cycle of the product and identify the potential problems timely so as to make better production schedule.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Chart Description Generation in Cyber–Physical–Social System

Chen

Zhao

2021

Symmetry

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There is an increasing use of charts generated by the social interaction environment in manufacturing enterprise applications. To transform these massive amounts of unstructured chart data into decision support knowledge for demand-capability matching in manufacturing enterprises, we propose a manufacturing enterprise chart description generation (MECDG) method, which is a two-phase automated solution: (1) extracting chart data based on optical character recognition and deep learning method; (2) generating chart description according to user input based on natural language generation method and matching the description with extracted chart data. We verified and compared the processing at each phase of the method, and at the same time applied the method to the interactive platform of the manufacturing enterprise. The ultimate goal of this paper is to promote the knowledge extraction and scientific analysis of chart data in the context of manufacturing enterprises, so as to improve the analysis and decision-making capabilities of enterprises.

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Section: Introductionmentioning

confidence: 99%