It is envisioned that emerging 6G networks will introduce Integrated Sensing and Communication  (ISAC), a shift that transforms traditional wireless infrastructure into a ubiquitous sensor network. Unlike conventional sensing pipelines, ISAC data is often passively and opportunistically collected,  highly heterogeneous, noisy, and strongly coupled across space and time. These characteristics pose challenges that differ substantially from those assumed in many existing data mining and machine learning approaches.

This workshop views ISAC not simply as a new application area, but as a distinct data mining setting that raises fundamental questions about how data should be represented, analyzed, and interpreted. The close interaction between sensing and communication introduces feedback between data acquisition and inference, resource‑dependent observation processes, and cross‑layer dependencies, which complicate standard workflows for spatiotemporal analysis, multimodal fusion, streaming learning, and distributed data mining. Addressing these issues requires data‑centric perspectives that explicitly account for the unique properties of ISAC data. 

 Organizers