## 3 Proposed Pipeline
### Inspect  
Large amounts of collected records, especially related to the spatial motions of 6-degree-of-freedom (6 DoF) actors--like virtual cameras, HMD controllers, hands, etc.--in virtual or physical 3D spaces, even when filtered or processed, raise several challenges in terms of data presentation and inspection for sense-making and analysis. Novel ways to visualize, present and interpret such data can offer huge support to decode interaction patterns and provide analysts with advanced interfaces to interact with records and gather insights about users' sessions. The third stage of our pipeline thus consists of a collaborative immersive analytics tool, targeting the inspection of captured interactive sessions in virtual (or physical) 3D spaces. The resulting open-source tool (called _\"Merkhet\"_) is being developed under the H2 IOSC project (pilot 7.7) on top of the ATON framework [24]. Thus, it is fully web-based and accessible by remote analysts using standard browsers, without any installation required. The tool indeed inherits the XR functionalities and spatial UIs offered by the underlying framework [22] through the WebXR protocol, but also benefits from the _plug-and-play  
Figure 3: (**A**) File system structure enabling asynchronous capture block service and process block service. (**B**) Branching of processed and analyzed data versions due to collaborating analysts. architecture for progressive web applications (or PWA [47]), improved through H2 IOSC in the servification Work Package. The first piece of information that the tool requires is the ATON scene ID (see [24], Section 3.2), representing the 3D space (or a digital replica of a physical space) where the users performed their interactions. The scene may contain a single object or a more complex virtual space with a large spatial extent. Such an ID corresponds indeed to the \(groupID\) described in Section 3.1. Once the virtual 3D space is loaded, the tool can load the associated records (captured users' sessions or aggregates processed in previous stage) via a user interface (see Figure 4, top row). This provides the direct visual mapping of volumetric data with the original virtual space. Analysts can request either raw data (see Section 3.1) or processed/filtered records (see Section 3.2) from the remote hub. Once a spatio-temporal record is loaded, a volumetric trajectory is realized with the associated data, suitable for standard or XR inspection. Multiple records can be loaded at once for comparison, in-depth inspection or to study more advanced interaction patterns. Merkhet's non-immersive interface presents a slider to control the time in the active record (see Figure 4, bottom row), thus enabling playback functionalities for the analyst while inspecting the captured session. Given the location, view direction and field-of-view attributes, it is also possible to recreate the original virtual camera settings and align the analyst's view, thus constructing the history of the session from the same perspective as the participant. It is also possible to load the aggregate data generated in the previous stage (Section 3.2): this generally involves single or multiple records per scene, enabling the analysts to inspect volumetric data supporting the detection of interaction patterns. Such data can be explored interactively by the remote analyst through standard or immersive VR devices, and numeric information--such as KDE density values (see Figure 5A,B)--can be included. It also employs the renewed \"_Photon_\" component (see [24], Section 3.7), enabling synchronous collaboration among remote participants--in this case, analysts. This allows two or more remote analysts to not only operate synchronously in the same virtual 3D space using VR equipment, but also to discuss together the data that they are inspecting, directly from a standard web browser. This section describes the _Merkhet_ functionalities offered to remote analysts on the web, accessible on mobile devices, desktop devices or XR devices.