Search

Internet big data have provided new data sources for timely analysis and accurate forecasting of economy, finance, and other industries. With the development of tourism industry in China, it is necessary to incorporate more accurate data sources for tourism demand and tourism management. This paper collected two kinds of Internet big data to examine the application of big data in Huairou, Beijing. We first crawled the Internet search data from Baidu search engine, and used principle component analysis to construct search index in order to represent tourism demand. Then, we used online travel data generated from Ctrip to depict the tourists' characteristics such as preference to tourism spots and hotels. Based on the empirical results, we offer suggestions for tourism management. This paper will provide innovative perspectives for tourism management in the big data era. [ABSTRACT FROM AUTHOR]

This paper uses Baker et al. (2016) to construct China's economic policy uncertainty index (EPU) and China's economic policy uncertainty index based on Chinese newspaper text keyword search (CEPU). Using monthly import data of about 5000 kinds of products in China from January 2010 to April 2016, we estimate the impact of EPU and CEPU on these products' import and find that economic policy uncertainty has different effects on imports of different products, both negative and positive. Further, we use BEC classification, differentiated products and homogeneous products, the number and concentration of countries in global trade, and the elasticity of import demand to characterize the heterogeneity of products and explain the differences in the impact of economic uncertainty on different products. We find that when the uncertainties of China's economic policy increase, capital goods and transport equipment are more negatively affected by imports than intermediate inputs and differentiated products are homogeneous products; the smaller the elasticity of demand substitution, the more countries imported globally, the lower the concentration of global importing countries, and the higher the concentration of global exporting countries, the negative reaction of product imports the bigger. In addition, we compare the performance of EPU and CEPU indices. CEPU index is more reasonable than the EPU index. [ABSTRACT FROM AUTHOR]

"Big data + simulation model + post-simulation analysis" is viewed as a new research paradigm in the context of big data. To investigate an appropriate post-simulation analysis tool for the new paradigm, this paper proposes a new screening methodology based on using elementary effects, Bootstrap hypotheses testing, together with false discovery rate error techniques (abbreviated BFEE). BFEE is capable of precisely identifying and mining the most important factors among a large number of factors in simulated systems. Compared to the conventional elementary-effects-based method, BFEE guarantees accuracy and efficacy of simulation experiments. Moreover, BFEE exhibits higher flexibility and is easily adapted to the big data context in comparison with sequential bifurcation, an alternative screening method in simulation. The comparative analysis of Monte Carlo simulations indicate that the proposed method achieves desired simulation efforts and statistical precision, without the need for overmuch assumptions. The corresponding results from a real-world case study shows the potential applications in practice. [ABSTRACT FROM AUTHOR]

How to carry out statistical learning, prediction and optimization of big data in the situation of high-dimensional for realizing the data-driven decision making has become an important issue for the management science and system engineering. To explore how to apply the theory and methods of big data into the engineering project management, firstly, this paper analyzes the value chain of data-driven decision making in engineering management. Then, we propose the three-dimensional structure model of the engineering project management based on the big data, which presents from the time dimension (the phase of the project life cycle), domain dimension (project management domains) and method dimension (big data analysis method) to promote the research and application of big data in engineering project management. Furthermore, the data involved in each stage of the life cycle are summarized, the potential application domain of big data methods in various management domains of the complex engineering project are analyzed. Finally, an industrial example is provided to illustrate the application of big data technology in the engineering project and the related scientific problems are discussed. [ABSTRACT FROM AUTHOR]