###### Abstract  
Recording large amounts of users' sessions performed through 3D applications may provide crucial insights into interaction patterns. Such data can be captured from interactive experiences in public exhibits, remote motion tracking equipment, immersive XR devices, lab installations or online web applications. Immersive analytics (IA) deals with the benefits and challenges of using immersive environments for data analysis and related design solutions to improve the quality and efficiency of the analysis process. Today, web technologies allow us to craft complex applications accessible through common browsers, and APIs like WebXR allow us to interact with and explore virtual 3D environments using immersive devices. These technologies can be used to access rich, immersive spaces but present new challenges related to performance, network bottlenecks and interface design. WebXR IA tools are still quite new in the literature: they present several challenges and leave quite unexplored the possibility of synchronous collaborative inspection. The opportunity to share the virtual space with remote analysts in fact improves sense-making tasks and offers new ways to discuss interaction patterns together, while inspecting captured records or data aggregates. Furthermore, with proper collaborative approaches, analysts are able to share machine learning (ML) pipelines and constructively discuss the outcomes and insights through tailored data visualization, directly inside immersive 3D spaces, using a web browser. Under the H2 IOSC project, we present the first results of an open-source pipeline involving tools and services aimed at capturing, processing and inspecting interactive sessions collaboratively in WebXR with other analysts. The modular pipeline can be easily deployed in research infrastructures (RIs), remote dedicated hubs or local scenarios. The developed WebXR immersive analytics tool specifically offers advanced features for volumetric data inspection, query, annotation and discovery, alongside spatial interfaces. We assess the pipeline through users' sessions captured during two remote public exhibits, by a WebXR application presenting generative AI content to visitors. We deployed the pipeline to assess the different services and to better understand how people interact with generative AI environments. The obtained results can be easily adopted for a multitude of case studies, interactive applications, remote equipment or online applications, to support or accelerate the detection of interaction patterns among remote analysts collaborating in the same 3D space. immersive analytics; data analytics; WebXR; collaborative systems; pattern detection; time series analysis; spatial interfaces +
Footnote †: journal: _Future Internet_**2024,**_16,_147. [[https://doi.org/10.3390/616050147](https://doi.org/10.3390/616050147)]([https://doi.org/10.3390/616050147](https://doi.org/10.3390/616050147))
data understanding, while accelerating the analysis process itself. In order to support such tasks, immersive analytics (IA) systems have become a major topic of investigation and immediately presented specific benefits and challenges. Immersive spaces improve the quality and efficiency of the analysis process, when proper design solutions are provided to interact with volumetric data. The recent literature shows how the additional opportunity to share the virtual environment with other remote analysts improves the sense-making task and further accelerates the detection of interaction patterns. The H2 IOSC project's ([[https://www.h2](https://www.h2) iosc.cnr.it/]([https://www.h2](https://www.h2) iosc.cnr.it/), accessed on 22 April 2024) goal is to create a federated and inclusive cluster of research infrastructures targeting researchers from various disciplines. The services already offered by the involved RIs are being refactored to improve access, as well as to provide strong alignment with the FAIR principles and with international best practices.