The article addresses the problem of intelligent monitoring of facility management data flows collected from heterogeneous sources, including low-level data of sensors and probes, geographical indicators, scheduling and personal identification systems. We suggest the framework of analytical model, based on deriving descriptors which could sentinel the level of thermal comfort of working environments. The model aims to facilitate process of extracting essential characteristics of facility management for detecting dependencies and observing anomalies. The performance of the model was tested by experimental analysis of facility management of the university campus, designed for exploring how various environment variables affect temperature in the lecture rooms, equipped by the air conditioning devices.
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