Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Discovering users' whereabouts patterns is important for many emerging ubiquitous computing applications. Life-log systems, advertisement and smart environments are only some of the applications that can be supported by information regarding user patterns and routine behaviors. Latent Dirichlet Allocation (LDA) is a porful mechanism to extract recurrent behaviors and high-level patterns (called topics) from mobility data in an unsupervised manner.   test the effectiveness of LDA in identifying users' routine behaviors from mobility data collected with Google Latitude. Results show that the proposed technique provides good results in discovering patterns and routine behaviors.
['Laura Ferrari', 'Marco Mamei']
I am writing a research paper on a idea Discovering daily routines from Google Latitude with topic models
[{"from": "human", "value": "I am writing a research paper on a idea Discovering daily routines from Google Latitude with topic models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Discovering users' whereabouts patterns is important for many emerging ubiquitous computing applications. Life-log systems, advertisement and smart environments are only some of the applications that can be supported by information regarding user patterns and routine behaviors. Latent Dirichlet Allocation (LDA) is a porful mechanism to extract recurrent behaviors and high-level patterns (called topics) from mobility data in an unsupervised manner.   test the effectiveness of LDA in identifying users' routine behaviors from mobility data collected with Google Latitude. Results show that the proposed technique provides good results in discovering patterns and routine behaviors."}]