Research On Cognitive Dimensionality Reduction For Big Data Visualization Interface

With the coming of the big data era,users will be exposed to many high-dimensional data every day.With the rapid development of the science and technology of the Internet age,the quantity and dimensions of data have continuously increased,and the high-dimensional data has become an important part of big data.High dimensional data with high complexity hides a large amount of valuable information,mining high-dimensional data and visualizing it that can help people obtain more information and its deeper meaning.But because of the complexity and multidimensional attributes of the large data,which represent the large amount of information and the complex hierarchical structure,resulting in high fatigue and low efficiency during users' cognitive process.At present,there are many ways to reduce dimension of data.Although it reduces the dimensions of high-dimensional data,it lacks consideration of human cognition.Moreover,because of the limitation of human cognition,the dimension reduction of existing methods will impose certain restrictions on the user's operational efficiency.Therefore,this topic mainly combines the characteristics of human cognitive behavior,through the study of big data characteristics,big data visualization features and the lack of existing descending dimension methods,analyzes in process of large data visualization,the mapping relationship between the user's cognitive dimension and the visual interface information dimension,and puts forward the theory method of dimension reduction and its corresponding design strategy.First of all,through the attributes and visual characteristics of high-dimensional data,we can analyze the importance of visualization and the dimension reduction.Secondly,combining the cognitive process of people interacting with the big data visualization interface,propose the concept of cognitive dimensions and define and classify them.And correspond to each dimension,respectively propose interface-related information dimensions and information attribute codes.And the mapping relation model of cognitive dimension to interface information dimension(C-I)is established,which highlights the high dimensions of each dimension.Then,based on the information characteristics of high dimensional data and the cognitive requirements of the users,we propose the dimension homologous clustering,information filtering and other theoretical methods,and then proceed from the surface information,internal structure and deep meaning of the visual interface,and carry out the research of cognitive reduction strategy design.Finally,the theory method is applied to the practical design practice of the visual interface of large data.And the purpose of its application is to improve the efficiency and accuracy of the user's operation.