Introduction of powerful mobile devices and increasing availability of online services make it possible to develop a wide range of mobile applications. Making recommendations to the users on their mobile devices based on their location is a wellknown application area of location based services. In this work we introduce an ontology based approach to find reasonable recommendations for sites (Points of Interest) like restaurants, hotels, and touristic places. We extend an existing OWL ontology in order to keep semantic relationships between different site types. Our application populates this ontology with site instances collected from several data sources, namely Google Maps, GeoNames, DBpedia and a local database. During this integration process, solutions for ontology mapping, site categorization, and duplicate site detection are developed. The ontology is then used to make recommendations on a mobile augmented reality application based on user's inputs on his device.
The use of smartphones in Intelligent Transportation Systems is gaining popularity, yet many challenges exist in developing functional applications. Due to the dynamic nature of transportation, vehicular social applications face complexities such as developing robust sensor management, performing signal and image processing tasks, and sharing information among users. This study utilizes a multimodal sensor analysis framework which enables the analysis of sensors in multimodal aspect. It also provides plugin-based analyzing interfaces to develop sensor and image processing based applications, and connects its users via a centralized application as well as to social networks to facilitate communication and socialization. With the usage of this framework, an on-road anomaly detector is being developed and tested. The detector utilizes the sensors of a mobile device and is able to identify anomalies such as hard brake, pothole crossing, and speed bump crossing. Upon such detection, the video portion containing the anomaly is automatically extracted in order to enable further image processing analysis. The detection results are shared on a central portal application for online traffic condition monitoring.
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