Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data

Rübel, Oliver; Ahern, Sean; Bethel, E. Wes; Biggin, Mark D.; Childs, Hank; Cormier‐Michel, E.; DePace, Angela H.; Fowlkes, Charless C.; Geddes, C. G. R.; Hagen, Hans; Hamann, Bernd; Huang, Min-Yu; Keränen, Soile V. E.; Knowles, David; Hendriks, Cris L. Luengo; Malik, Jitendra; Meredith, Jeremy; Messmer, Peter; Prabhat,; Ushizima, Daniela; Weber, Gunther H.; Wu, Kesheng

doi:10.1016/j.procs.2010.04.197

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…This article combines literature measurement, visual analysis, social network analysis and other methods to analyze the status of cultural added value research [3][4][5] . The research ideas and technical route are shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

The Development Trend of Cultural Added Value Research Based on Big Data Analysis

Yudong

et al. 2020

E3S Web Conf.

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Cultural added value refers to the value added generated by the combination of cultural elements and different forms of carrier. It shapes the uniqueness of cultural products, not only satisfies the cultural spiritual enjoyment of consumers and helps enterprises build a unique cultural brand image, but also promotes excellent traditional culture and spreads positive cultural values. However, its research is still in an initial stage. The relevant concepts and context are not clear enough and lack of systematic combing. Therefore, this paper takes 354 relevant articles as data samples for the following analysis. Firstly, we used the method of bibliometric analysis to sort out its publishing trend and regional distribution. Secondly, we used social network analysis method to identify research hotspots in this field and analyzed its evolution trend. Thirdly, cluster analysis was used to identify relevant research topics in the field of cultural appreciation, and literature review was conducted around these three clustering topics. Finally, the future research direction of this field is proposed based on the above contents.

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Section: Methodsmentioning

confidence: 99%

The Development Trend of Cultural Added Value Research Based on Big Data Analysis

Yudong

et al. 2020

E3S Web Conf.

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“…VR takes advantage of the computer technological development and scientific visualization to create a virtual world [ 4 ]. The use of VR has become very popular because it offers a high level of realism and immersion but requires advanced computing technologies capable of processing large amounts of scientific data and graphics [ 5 ]. VR has been used in different areas such as engineering, medicine, education, entertainment, astronomy, archaeology, and arts.…”

Section: Introductionmentioning

confidence: 99%

On the Development of Virtual Reality Scenarios for Computer-Assisted Biomedical Applications

Govea-Valladares

Medellín-Castillo

Ballesteros

et al. 2018

Journal of Healthcare Engineering

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The modelling of virtual environments and scenarios is an important area of research for the development of new computer-assisted systems in the areas of engineering and medicine, particularly in the area of biomechanics and biomedical engineering. One of the main issues while designing a virtual environment is the level of realism, which depends on the computing capacity and the level of accuracy and usefulness of the generated data. Thus, the dilemma is between the aesthetic realism and the information utility. This paper proposes a methodology to develop low-cost and high-quality virtual environments and scenarios for computer-aided biomedical applications. The proposed methodology is based on the open-source software Blender and the Visualization Toolkit libraries (VTK). In order to demonstrate the usability of the proposed methodology, the design and development of a computer-assisted biomedical application is presented and analysed.

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“…Designers of multidimensional(MD) models must structure the information that is available into facts and dimensions. Facts are usually measures of business processes of some kind and dimensions represent the different ways in which the data can be viewed and sorted [3]. Business intelligence(BI) systems extend this by using heuristic and statistical models to find interesting features and make predictions.…”

Section: Introductionmentioning

confidence: 99%

Metadata Based Visualization System for Multidimensional Data

Peng¹

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-With the ability to measure and simulate more processes at finer scales, the number of data dimensions and data objects has grown significantly in today's datasets. Much existing research has focused on different visualizations for different data exploration tasks. In this paper, based on the financial management data of MAIN company, a financial management system is analyzed and designed, meanwhile, MAIN's important financial data are extracted, classified, and sorted out. with the analysis of financial data, a multidimensional (MD) data model based on the metadata was implemented; the system architecture and visualization exhibition pattern are designed based on BI(Business Intelligence) technology and an existing metadata management system; The Visualization Exhibition System was implemented by ASP.NET and Silverlight Technology. This system provides multiple perspectives and comprehensive query analysis functions for corporate decision makers and managers. It also provides a feasible solution of BI front-end display system for small and medium-sized enterprise.

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Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data

Cited by 10 publications

References 18 publications

The Development Trend of Cultural Added Value Research Based on Big Data Analysis

The Development Trend of Cultural Added Value Research Based on Big Data Analysis

On the Development of Virtual Reality Scenarios for Computer-Assisted Biomedical Applications

Metadata Based Visualization System for Multidimensional Data

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