## 5 Conclusions  
We presented and discussed a web-based, collaborative, open-source pipeline to capture (Section 3.1), process (Section 3.2) and inspect (Section 3.3) remote interactive sessions, providing a modular immersive analytics system to accelerate the detection of interaction patterns among remote analysts and support sense-making tasks. The pipeline components can be deployed on a dedicated hub (or multiple hubs) targeting public exhibits, research infrastructures, lab facilities, interactive 3D/XR applications or advanced equipment (e.g., HMDs, motion-tracking devices or sensors, BCI headsets, etc.). We assessed the pipeline and its advantages through a WebXR application presenting generative AI content to visitors in two remote public exhibits (Section 4), transmitting anonymous spatial data to a remote hub via an internet connection. Generative AI services gave us the possibility to greatly accelerate the content creation workflows and study the impact on visitors through a set of immersive experiences. Although, in this work, we focused on the spatial interaction in virtual 3D scenes, the capture service that we developed (Section 3.1) offers full abstraction in terms of state attributes, alongside a formalized REST API (following the guidelines developed in H2 IOSC). This makes it suitable for a wide range of scenarios, including the tracking of custom properties for remote devices, remote infrastructure equipment, online applications or motion tracking sensors. Tracking actors' attributes in physical spaces is also possible if proper equipment is employed--for instance, in museums [52], CAVEs, motion tracking suits, GPs-based location tracking in web applications and much more. Figure 13: (**A**) Two generative 3D items; (**B**) sample AR presentation during TourismA exhibit on the table; (**C**) inspection of a session captured on one object; (**D**) AR inspection through a smartphone; (**E**) inspection of KDE for users’ motions in the physical space; (**F–H**) mixed reality inspection through Meta Quest PRO with a remote analyst. The central processing stage (Section 3.2) offers a suite of web-based services for analytics workflows to examine and filter incoming raw data and then process them on the hub using machine learning models. This workflow is designed for teams that collaborate in implementing and comparing different machine learning models, and it allows a natural extension to deep neural network models and other cutting-edge machine learning methods. The next step for this stage will be to integrate compact image-based encoding models such as \"PRISMIN\" [53; 54], in order to improve the exchange, transfer, manipulation and comparison of data records and aggregates among different peers/hubs over the network, also facilitating new machine learning approaches. We designed and developed \"Merkhet\" (Section 3.3), a WebXR immersive analytics tool offering advanced features to inspect, query and annotate data records and aggregates. Thanks to the underlying ATON framework, the tool can be accessed anywhere on mobile devices, desktop devices and especially immersive headsets, providing spatial interfaces to interact with data in XR modes. Furthermore, the tool offers synchronous collaborative inspection among remote analysts, allowing remote professionals to discuss, together in the same virtual space, session records or data aggregates. Through the assessments that we carried out in Section 4, the tool was demonstrated (1) to accelerate the detection of interaction patterns and support insights; (2) to facilitate inspection and correlation tasks due to the volumetric overlay of session data and the original virtual space where the visitors were recorded; (3) to accelerate sense-making tasks thanks to the collaborative mode, with multiple analysts discussing and annotating the data in real time; (4) to offer augmented/mixed reality inspections carried out in different physical spaces by multiple online analysts on the same virtual scene (see Section 4.6), using only a standard web browser.