Study On The Complexity Of Big Data Visualization Based On User Cognition

Along with the rapid development of internet,cloud computing,artificial intelligence and other technologies,big data visualization not only requires to present huge amounts of data information,also contains lots of real-time variety,multidimensional and dynamic interaction of form,however,the current big data visualization rarely considers the user's cognitive demand,resulting in many valuable information lost in the "complexity" during users reading.Therefore,the research on the complexity of big data visualization from the perspective of user cognition is a key issue in the research fields between User Design and Data Visualization.This study takes user cognition of big data visualization as the starting point,through behavioral and physiological experimental methods,comprehensively carried out the complexity research from four aspects: cognition,data,vision and interaction.The research results provided effective research ideas and method guidance for the complexity optimization of big data visualization.The research priorities and innovation points were listed in the following.(1)We made a targeted study on the information processing process,cognitive load and typical cognitive mechanism of big data visualization users.On the basis of these,this paper successively proposed the information processing model,cognitive load structure model and cognitive complexity structure model of big data visualization,and fundamentally sorted out the constituent factors of cognitive complexity in visualization.(2)We proposed a data structure based on user's cognitive space,and realized the reconstruction of the data structure by R software.We established the representational mapping relationship between the data structure and the primitive encoding,filling the research gap from data to visualization.Then,based on experimental methods,we explored the influence of the number and superposition of multi-attribute encoding on the user's cognitive performance.(3)Through theoretical analysis and experimental methods,we determined the objective attributes and subjective factors that constitute the visual complexity of big data visualization,as well as the validity of the theory of hierarchical mapping of visual complexity.Our research indicated that visual complexity is essentially determined by users' chunking ability and the strength of chunks of visual elements.The more familiar one visualization is,the higher chunking strength user has and the lower the visual complexity it becomes.These findings made a theoretical contribution to the study of visual complexity.(4)We decomposed the interaction complexity of big data visualization from the three levels of interaction operation,interaction behavior and interaction logic,and discussed the relationship between the complexity and redundancy.Combined with all the research results,we proposed a structural model of the overall complexity of big data visualization,and a serious of detailed complexity optimization design methods,design process and reverse analysis method.After applying these methods to case design,we verified the effectiveness of these methods.These methods could provide an accurate and fast guidance for the design and analysis of big data visualization.