Zhi Xiao

To process a huge amount of data, computing resources need to be organized in clusters that can be scaled out easily. Still, traditional SQL databases built on the relational data model are difficult to be put to use in such clusters, which has motivated the movement named NoSQL. However, NoSQL databases have their limits by using their own data models. In this paper, the original soft set theory is extended, and a new theory system called n-tier soft set is brought up. We systematically constructed its concepts, definitions, and operations, establishing it as a novel soft set algebra. And some features of this algebra display its natural advantages as a data model which could combine the logicality of the SQL model (also known as the relational model) and the flexibility of NoSQL models. This data model provides a unified and normative perspective logic for organizing and manipulating data, combines metadata (semantic) and data to form a self-described structure, and combines index and data to realize fast locating and correlating.

Search engines play an important role in providing us with the main information of our daily life. The research on the search behavior on the Internet enjoys greater and greater popularity, for the search behavior has been proved to affect our daily decisions in purchasing, traveling, and even defining beauty. However, there is still a lack of full appreciation of the relation between the search behavior itself in terms of the emotional meaning and the decisions thus generated. Therefore, this study was carried out to analyze the emotional meanings of 13,915 English words obtained from Google Trends and the profits gained from the US house market by automatic transactions and discovered that the emotional meanings of the search contents could modulate the financial decision with unsupervised machine learning methods.

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Peer-to-Peer (P2P) lending brings many benefits for both lenders and borrowers, as well as risk for them, especially for the lenders. To help the lenders select a reliable P2P lending platform with high return and low risk, a more comprehensive selection method is necessary. However, vast information with fluctuant, fuzzy and subjective data on the P2P lending platform make it more difficult for lenders to make the right choice. This paper intends to propose a selection method by using intuitionistic fuzzy soft set to organize the data, and using D-S theory of evidence to integrate the intuitionistic fuzzy values. Besides, the paper analyses the critical factors affecting the lenders’ decision, and presents some indicators for evaluating. Finally, an empirical experiment was given by choosing several P2P lending platforms with real data, and the results are compared with the intuitionistic fuzzy weighted average and Development Index from Wangdaizhijia, that verify the validity and superiority of the proposed method.

The information that the brain receives from the senses is usually consistent with a range of possible stimuli. And therefore all of our perceptual decisions are almost made in an uncertain situation. This sensation of uncertainty affects behavior as we know it, but how this feeling to affect human behavior is unclear. The motivation of this paper is to provide empirical evidence for the relationship between financial market behavior and external stimulus information. We adopt a new approach that is completely different from the existing literature, relying on neuroscience and machine learning methods to explore how the brain perceives external stimulus information and ultimately influences financial market behavior. In this paper, we improve the BP neural network. The brain perception model is used to determine the input variables of the BP neural network. By this method, the number of input nodes of the BP neural network can be reduced to six and the mental process behind the stimulus is simulated. In addition, we optimize the parameters of the brain perception model to construct the optimal brain perception model for specific external stimuli. The training, test and evaluation exercises using the improved BP neural network show that the results are satisfactory and superior to other models.