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.