## 2 Related Work  
In order to perceive patterns and extract knowledge from large datasets and dynamic information streams, _Visual Analytics_[6] are often used by analysts, teachers or other professionals, to discover the unexpected and/or detect the expected. For instance, interactive installations deployed in public events or spaces, allow the ability to collect a large amount of data from casual visitors and then analyze them [7]. Data mining approaches are often employed for tourist activities discovering landmark preferences from photo mappings [8] while classical clustering methods [9] can be used offline to analyze spatial behaviors. Interaction with an immersive VR application is inherently 3D: in order to carry out consistent analysis on interactions performed in a virtual or physical 3D context, user sessions should be recorded using _volumetric_ approaches [10]. Locomotion in particular [11] is largely investigated in literature for the exploration of IVEs using a HMD. Detecting spatial 3D patterns may support learning assessment, discover users' patterns or assess the interaction model adopted for the VR application, including virtual classrooms, public/shared spaces, etc.  
appealing also for specific massive open online courses (MOOCs) that offer interactive VR training [14] or immersive sections to remote students within online, educational IVEs.  
There is a growing interest in _discovering and visualizing interaction patterns_ in immersive VR sessions. Regarding IVE saliency, most of these works focus on panoramic/omnidirectional content from fixed viewpoints (360-degree applications). The research carried out in [15] analyzes, for instance, how people interact with panoramic content (fixed viewpoint) recording and visualizing gaze data. A model to obtain fixations from head direction is investigated in [16], providing good approximations when eye-tracking systems are not available on the HMD. The research in [17], presents a robust metric and visualization approach to measure similarities between users scan-paths and director's cut using color-coded maps. Immersive analytics is also emerging as a research field to investigate how novel interaction models and display technologies can be employed to support analytical reasoning and decision making in 3D [18]. The main objective is to investigate advanced and usable user interfaces in order to support collaboration and offer VR analysts tools to immerse themselves in complex 3D datasets. A few recent works also focus their attention on immersive analytics for the Web using open-standards like WebVR/WebXR facing all the challenges related to online deployment. Research in [19; 20] for instance discusses some of the problems faced by developers in crafting effective and informative immersive web-based 3D visualizations. The combination of immersive analytics with the new WebXR API is fueling research in the field of data visualization, as it allows the VR analyst to better perceive some data difficult to understand using traditional techniques [21].  
In order to exchange large spatial records in a compact manner (e.g., coordinates, vectors, etc.), _image-based encoding_ can be used by transferring common 2D images. The PNG format [22], offers a network-friendly, cross-platform and lossless compression scheme particularly suitable for the Web. For instance, previous works adopted such format as externalized mesh container [23; 24] to efficiently stream geometry data over the network and to easily decode it by client web browsers. Previous encoding models in literature also investigated \"geometry images\" [25] as 2D arrays employed to quantize and store spatial information (\(<x,y,z>\)) as RGB values.