Interaction Design Of Relational Data Visualization In Immersive User Interface:

Relational data is a common type of data,and adjacency matrix is a common visualization model for describing relational data.Node attributes are important information in adjacency matrices,but traditional visualization methods can cause a series of problems(such as edge information occlusion,redundant information,and users repeated perspective switching),which can affect users’ usage and judgment,and ultimately lead to low efficiency in identifying and comparing node attributes during task completion.In recent years,Immersive Analytics(IA)has become a new technique for data analysis.In this paper,we leverage the spatial and interactive advantages of immersive environments to explore new models and interaction methods for visualizing relational data,enabling more efficient identification and comparison of multi-dimensional node attributes and improving the interaction flow for data exploration.The main contributions of this paper include three parts:1.Design and construction of a multivariate adjacency matrix model in an immersive user interface:Based on the pain points in the interaction process of identifying and comparing attributes in the multivariate adjacency matrix model,the MVF(Multivariate Fence)model was designed.Leveraging the advantages of the immersive user interface,the mapping,position,and layout of each element in the model were correspondingly aligned with the target.2.Design and construction of an interaction system in an immersive user interface:Using embodied and proxemic interaction,an interaction technique that uses body position information data for operation is designed and implemented,thereby shortening the interaction process steps.3.Comparative study of multi-attribute adjacency matrix models in immersive user interfaces:design data exploration tasks for identifying and comparing node attributes and compare the performance with traditional multi-attribute adjacency matrix models.Finally,according to the experimental results,it is proved that the MVF model is superior to the traditional model in identifying,comparison,understanding ability and user preference.