  The fifth generation (5G) and beyond wireless networks are foreseen to
operate in a fully automated manner, in order to fulfill the promise of
ultra-short latency, meet the exponentially increasing resource requirements,
and offer the quality of experience (QoE) expected from end-users. Among the
ingredients involved in such environments, network slicing enables the creation
of logical networks tailored to support specific application demands (i.e.,
service level agreement SLA, quality of service QoS, etc.) on top of physical
infrastructure. This creates the need for mechanisms that can collect
spatiotemporal information on users'service consumption, and identify
meaningful insights and patterns, leveraging machinelearning techniques. In
this vein, our paper proposes a framework dubbed"SOCLfor" the Service Oriented
CLustering, analysis and profiling of users (i.e., humans, sensors, etc.) when
consuming enhanced Mobile BroadBand (eMBB) applications, internet of things
(IoT) services, and unmanned aerial vehicles services (UAVs). SOCL relies
mainly on the realistic network simulation framework"network slice
planne"(NSP), and two clustering methods namely K-means and hierarchical
clustering. The obtained results showcase interesting features, highlighting
the benefit of the proposed framework.
