Future 5G network ecosystems comprise a plethora of 3GPP and non 3GGP Radio Access Technologies - RATs. Deployment scenarios envision a multi-layer use of macro, micro and femto-cells where multi-mode end devices, supporting different applications, are served by different technologies. The association of end devices to the most appropriate RAT/layer will therefore become a tantalizing process necessitating the introduction of mechanisms that decide and execute an optimal mapping. The latter is of paramount importance since sub-optimal configuration of network components will affect overall network performance. Towards this end, we introduce the Context Extraction and Profiling Engine (CEPE), a knowledge discovery (KDD) framework catering for the extraction and exploitation of user behavioral patterns from network and service information. An eNB exploits the knowledge scheme derived by CEPE in order to improve the placement of end devices to RATs/layers. In the context of this paper, we provide a thorough analysis of existing standards, research papers and patents, discuss the main innovation of our proposal and highlight the differences with existing schemes. Building on use cases involving mobility management mechanisms that typically affect device to technology mapping (i.e. cell (re)selection, handover) we provide an extensive set of experiments that demonstrate the validity and viability of our idea. Overall evaluation showcases that CEPE achieves high quality results thus emerging as a viable approach for network optimization in future 5G environments.