Optimization And Expansion Of Big Data Visualization Based On Multimodal Theory

The visual design of data as the main way to present data information has played an important role in people's understanding of data,discovery of information and decision making.However,information visualization has faced many challenges of big data features such as large,fast,multi-source,heterogeneous in the era of big data,which means traditional single modal display of data-centric can not meet complex and diverse application requirements of multi-modal spatiotemporal data.And the rapid development of information carriers such as 3D,animation,interaction,VR,AI and etc.,which provide more innovative breakthroughs for visualization.In the context of opportunities and challenges,The optimization approach and innovation development ideas of big data visualization is proposed.This paper begins with the multi-modal concept and explains the meaning of multimodality from multidisciplinary perspective.Secondly,it analyzes the multimodality of big data and the cognitive process of multimodal fusion when people acquire and interpret information.And the concept of multi-modal was introduced to the display method,discussing the multi-modal content presentation in visualization,and the dynamic interaction mode combined with high-tech.The paper will also discuss the innovation path of big data visualization specifically based on the modal fusion in the big data environment,the development of propagation carrier and the analysis of symbolic meaning of the information symbol,etc.Combined with the trend of big data visualization in the future,the two directions of "presentation and exploration" are put forward,which are proposing optimization strategies for the problems existing in the development of current visual form design and providing exploration ideas for future visual expansion combined with the latest technology.