Abstract

Multiple attribute decision making (MADM) is an efficient way to solve complex systems, and has wide application. This research develops MCDM model based on uncertain theory, used for selecting a suitable visualization alternative for tourism. First, in order to achieve desirable decision making, a new concept is proposed, which is called the best and the worst reference uncertain linguistic variable as a datum uncertain linguistic variable. At the same time, a new method for ranking uncertain linguistic variable is also presented. Second, based on the preference order relation of attributes given by the experts, a new score function is introduced to get the weight vector of attributes. Finally, the evaluation system of tourism big data visualization alternatives is constructed and the order of those alternatives is acquired by the decision method.

Highlights

  • Big data [1] has been risen as national strategic resources

  • The characteristics of tourism big data visualization alternative can be describes by a group of factor attributes, and experts can evaluate it with these attributes

  • There are many types of visualization alternatives faced by traveler, so it is hard to see which one is better

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Summary

Introduction

Big data [1] has been risen as national strategic resources. In recent years, after many developed countries such as Europe, the United States, Japan and South Korea took large data as a national level strategy, China have brought the construction of large data into the national strategic choice. It is significant to study the decision making of tourism big data visualization It can promote the fusion and innovation of data mining, analysis techniques and methods, computer graphics technology and decision theory and methods, and have a. Transformative influence on thinking and methods of government departments, tourism enterprises and tourists It can provide a more rapid, effective and scientific decision-making protection. In the process of decision, there are some difficulties for experts to express their preference degrees with crisp numerical values. It is another possible way to use linguistic labels [3], which represents qualitative aspects values. Moreovera new ranking method is presented to rank uncertain linguistic variable, which is based on the best and the worst reference uncertain linguistic variable

Preliminaries
The solution approach
The establishment of the evaluation
Conclusion
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