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In this paper, we present an integrated framework, named NaviComf, which constructs pedestrian navigation systems for comfort in varying environments by using multi-modal sensing technologies. With NaviComf we aim to systematically provide solutions to three key problems: (1) how to build the environmental data warehouse (EDW) which works as an infrastructure providing comprehensive and predictive environmental information, (2) how to integrate heterogeneous environmental information from multi-modal sensors into an aggregate value which facilitates further processing, and (3) how to determine the optimal path plans in environments which are varying continuously. In NaviComf the multidimensional data model and data prediction method are applied to build the EDW. Then a novel multi-factor cost (MFC) model is proposed as the fundamental concept to integrate the multi-modal sensor data. Based on the former two solutions, the optimal path planning (PP) problem is solved in a time-dependent network by applying a dynamic programming method. In the evaluations of NaviComf, sensor data for temperature, humidity, and pedestrian traffic flow have been gathered in real environments and a prototype system has been implemented with the data. Evaluations are conducted by using the prototype system and the results show that NaviComf can efficiently navigate pedestrians through more comfortable paths as compared to the traditional navigation method.