Evaluation on Big Data Industry Development Level Based on Hesitant Fuzzy Linguistic TOPSIS Method

DOI

10.2991/wrarm-19.2019.12How to use a DOI?

Keywords

Big Data Industry; Development Level of Big Data Industry; Baidu Index; Hesitative Fuzzy Linguistic Term Set; TOPSIS Method

Abstract

Reasonable and effective evaluation of the development level of big data industry is conducive to promoting the development of big data in an all-round way and speeding up the construction of a data power. In view of the insufficient information of the existing index system and the lack of fuzziness and hesitation in evaluation methods, this paper introduces Baidu search index to construct the evaluation index system of provincial large data industry development level from five aspects: industrial scale, product type, investment attraction, infrastructure and industrial influence; and chooses 2013-2017. Based on the original data of annual indicators, this paper evaluates the development level of big data industry in China by using TOPSIS method. The empirical results show that the big data industry in China is in the stage of continuous development in terms of time, and that in terms of space, Guangdong and Jiangsu have relatively high level of development, ranking first and second in the country respectively. During the sample period, the national big data industry is in a state of continuous development, but there is an imbalance in regional development.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lujing Pang
AU  - Mu Zhang
PY  - 2019/09
DA  - 2019/09
TI  - Evaluation on Big Data Industry Development Level Based on Hesitant Fuzzy Linguistic TOPSIS Method
BT  - Proceedings of the Sixth Symposium of Risk Analysis and Risk Management in Western China (WRARM 2019)
PB  - Atlantis Press
SP  - 63
EP  - 68
SN  - 1951-6851
UR  - https://doi.org/10.2991/wrarm-19.2019.12
DO  - 10.2991/wrarm-19.2019.12
ID  - Pang2019/09
ER  -