Research on Comprehensive Evaluation of Data Source Quality in Big Data Environment

Abstract

Data quality is the prerequisite of big data research and the basis of all data analysis, mining, and decision support. Therefore, a comprehensive fuzzy evaluation method for big data quality evaluation is proposed. Through the analysis of big data quality characteristics, a big data quality evaluation system for the whole process of data processing is constructed. The subjective weight and objective weight of each indicator are calculated through the analytic hierarchy process and entropy method. In order to overcome the subjective and one-sided shortcomings of the single weight determination method, the subjective weight and the objective weight are organically integrated through the distance function method to determine the combined weight of each indicator. The quantified result of big data quality is obtained through fuzzy calculation of membership degree. Finally the ranking results of the proposed method are compared with those of some existing multi-attribute decision-making (MADM) methods. The obtained results indicate that the proposed method is reasonable and efficient to deal with MADM problems. It can comprehensively measure the level of big data quality, and provide users with accurate and efficient quality evaluation results.

Factors Influencing the Information Adoption from Social Media review Platform: Extending Information Adoption Model (IAM) with Information Diagnosticity

“…Filieri et al (2021) mentioned that the consumers' decision-making and behavioural intention are significantly influenced by the social media review's information quality. Social media user evaluates the quality of the review information based on its comprehensibility, completeness, timeliness, accuracy, and sidedness (Chen et al, 2021;Gesell et al, 2022;Li et al, 2021;Shin, 2022).…”