The use of filters for adaptive mobile mapping scenarios

Abstract

Location based services should communicate information that
is relevant to the user and personalized to his/her interests and needs. Existing LBS exploit ancillary information such as the user’s position, user profile, or time of day to personalize information delivery. However,
there are a variety of information sources that remain largely untapped in current LBS. These include data from other applications on the mobile device, Web 2.0 sources, or special sensors. They have the inherent ability to define relevant places, events, activities for the particular user; they also allow to derive spatio-temporal behavior patterns that adapt to context. Using appropriate filters, user-specific information can be mined from these additional ancillary data sources, hence allowing to
minimize user interaction, better personalize content, and generate more meaningful real-time map displays. This extended abstract hence proposes the use of different filters to further enable adaptation of mobile map applications to the user and his/her context.