Automatic detection of points of interest using spatio-temporal data mining

Location Based Services (LBS) are still in their infancy but they are evolving rapidly. It is expected to have more intelligent, adaptive and predictive LBS applications in the future, which can detect users’ intentions and understand their needs, demands and responses. To have such intelligent services, LBS applications should be able to understand users’ behaviours, preferences and interests automatically and without needing users to be asked to specify them. Then, using users’ current situations and previously extracted behaviours, interests and preferences, LBS applications could provide the most appropriate sets of services. This paper shows the application of data mining techniques over anonymous sets of tracking data to recognise mobility behaviours and extract some navigational user preferences such as Point of Interests (PoI) in a format of if-then rules, spatial patterns, models and knowledge. Such knowledge, patterns and models are being used in intelligent navigational services, including navigational decision support applications, smart tourist guides and navigational suggestion making apps

Making tourist guidance systems more intelligent, adaptive and personalised using crowd sourced movement data

Ambient intelligence (AmI) provides adaptive, personalized, intelligent, ubiquitous and interactive services to wide range of users. AmI can have a variety of applications, including smart shops, health care, smart home, assisted living, and location-based services. Tourist guidance is one of the applications where AmI can have a great contribution to the quality of the service, as the tourists, who may not be very familiar with the visiting site, need a location-aware, ubiquitous, personalised and informative service. Such services should be able to understand the preferences of the users without requiring the users to specify them, predict their interests, and provide relevant and tailored services in the most appropriate way, including audio, visual, and haptic. This paper shows the use of crowd sourced trajectory data in the detection of points of interests and providing ambient tourist guidance based on the patterns recognised over such data

Automatic detection of points of interest using spatio-temporal data mining

Location Based Services (LBS) are still in their infancy but they are evolving rapidly. It is expected to have more intelligent, adaptive and predictive LBS applications in the future, which can detect users’ intentions and understand their needs, demands and responses. To have such intelligent services, LBS applications should be able to understand users’ behaviours, preferences and interests automatically and without needing users to be asked to specify them. Then, using users’ current situations and previously extracted behaviours, interests and preferences, LBS applications could provide the most appropriate sets of services. This paper shows the application of data mining techniques over anonymous sets of tracking data to recognise mobility behaviours and extract some navigational user preferences such as Point of Interests (PoI) in a format of if-then rules, spatial patterns, models and knowledge. Such knowledge, patterns and models are being used in intelligent navigational services, including navigational decision support applications, smart tourist guides and navigational suggestion making apps