## 2 Related Work
### Immersive Analytics  
Immersive Analytics (IA) was defined in 2015 as \"the applicability and development of emerging user interface technologies for creating more engaging and immersive experiences and seamless workflows for data analysis applications\" and more recently as \"the use of engaging, embodied analysis tools to support data understanding and decision making\" [10]. A recent survey by [PERSON] et al. [9] provides an extensive overview of IA. The visual analysis of multi-dimensional data in immersive environment consists of four aspects: immersive visualization, multi-dimensional data, ultimate space, and interaction input. We summarize each in the following paragraphs. **Immersive visualization. ** Immersive visualization is an engaging, multi-sensory, and embodied analysis tool to support exploration, communication reasoning, and decision making. Recent advances in immersive technologies (VR, AR, MR, large display, tangible surfaces, etc.) show new opportunities to analyze, explore, and present data using these technologies [30]. It has been applied to applications such as information visualization and image immersion. It has proven effective in many other scientific applications, such as brain tumor analysis, archaeology, geographic information systems, geosciences, and physics [31]. Immersion technology provides a broader space, changes the traditional windows, icons, menus, pointer (WIMP) interaction paradigm, and provides a new interactive experience. **Multidimensional data. ** Analyzing multidimensional datasets to find insights and trends has been a popular topic within IA. Several visualization tools and case studies have emerged. DXR [32], ImAxes [31], IATK [33], and other tools that support multi-dimensional data rendering in immersive environments. In a case study, MARVIS [21] combined AR and mobile devices to display more data dimensions in different views in space, providing more possibilities for view combination. Personal Augmented uses large displays to combine with AR and proposes a design space suitable for multi-person collaborative data analysis. For example, [PERSON] et al. [34] found that users were faster and more accurate when using a flat layout with a small number of multiples. Still, as multiples increased, a half-circle layout provided the best performance. The visual layout is relatively fixed in the above cases, and the space is not fully utilized. **Ultimate space. ** DataHop [35] and Maps Around Me [36] have a higher degree of expansion and utilization of space. Maps Around Me expands multiple hierarchies of map data in a spherical layout and provides an automatic layout technique to help users quickly organize data locations. DataHop uses the process of user data exploration as the layout, which can not only expand the space of data display; but also record the process of user exploration. However, one can easily get lost in that environment. **Interaction input. ** IA changes the traditional input, abandoning the WIMP paradigm, usually using new input methods such as air gestures and head rotation. Regarding navigation, devices with six degrees of freedom (6 DOF) can recognize the movement of characters in space and support users to walk and observe directly. Other ways, such as teleportation, can be used to facilitate quick movement in large spaces. Gesture interaction technology is also maturing. One paper studied the effect of different input methods on low-level tasks in a 3D node-link graph [37; 38], and the results showed no effect on accuracy. Still, for complex graphs, gestures were significantly faster. Immersive interactive environments also have limitations, such as color distortion, perspective, and occlusion, which must be explored and solved. At present, VR is a relatively mature technology among interactive devices that can provide an immersive environment with a reasonably good effect on display resolution and color.