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Mobility path information of cellphone users play a crucial role in a wide range of cellphone applications, including context-based search and advertising, early warning systems, city-wide sensing applications such as air pollution exposure estimation and traffic planning. However, there is a disconnect between the low level location data logs available from the cellphones and the high level mobility path information required to support these cellphone applications. In this paper, we present formal definitions to capture the cellphone users' mobility patterns and profiles, and provide a complete framework, Mobility Profiler, for discovering mobile user profiles starting from cell based location log data. We use real-world cellphone log data (of over 350 K hours of coverage) to demonstrate our framework and perform experiments for discovering frequent mobility patterns and profiles. Our analysis of mobility profiles of cellphone users expose a significant long tail in a user's location-time distribution: A total of 15% of a user's time is spent on average in locations that each appear with less than 1% of time.