Semantically modeling mobile phone data for urban computing

Abstract

Urban computing aims to enhance both human life and urban environment smartly by deeply understanding human behavior occurring in urban area. Nowadays, mobile phones are often used as an attractive option for large-scale sensing of human behavior, providing a source of real and reliable data for urban computing. But analyzing the data also faces some challenges (e.g., the related data is heterogeneous and very big), and the general approaches cannot deal with them efficiently. In this paper, aiming to tackle these challenges and conduct urban computing efficiently, we propose a data integration model for the multi-source heterogeneous data related to mobile phones by using semantic technology and develop a semantic mobile data management system. © Springer International Publishing 2013.