Locator: a self-adaptive framework for the recognition of relevant places

A high number of algorithms for the recognition of users' relevant places exist. However, none of them provide an optimal solution across all users and scenarios. We present a preliminary design of Locator -- a self-adaptive framework for recognizing users' relevant places. Locator learns for different contextual situations, combinations of algorithms and location sensor data that achieve the best performance in recognizing relevant places. We conducted a 5-weeks study and collected sensor and ground-truth data from 6 users. Our preliminary results indicate the shortcomings of relying on one algorithm and sensor for recognizing places and thus motivates the rational behind our approach.