School of Engineering Department of Computer Science and Engineering 93 AI Meets Big Data: User Analytics and Personalized Recommendation Based on Location Data Supervisor: CHAN Gary Shueng Han / CSE Student: LI Ho Yin / CS Course: UROP1100, Fall Since generating reference points in floor plans is essential in indoor localization, this report analyzes approaches in floor plan recognition that can help to generate reference points by computers automatically. In particular, this report mainly investigates a deep learning network which is proposed by a previous research team and tries to perform preprocessing on the input image to help the pre-trained model generate a better prediction on the floor plan in HKUST. Although the prediction result is still far from successful to generate RPs, the features of the model will be discussed, and some insights will be provided which hope can help to the further development of indoor localization. AI Meets Big Data: User Analytics and Personalized Recommendation Based on Location Data Supervisor: CHAN Gary Shueng Han / CSE Student: LIU Ming Hay / MAEC Course: UROP1000, Summer Pervasive positioning aims to locate users seamlessly, whether indoors or outdoors. Currently, different formats of data and applications are needed for users to locate themselves, while users are entering another building or going outdoor. The implementation of pervasive positioning requires the correct format and correct parties to request location services. The Pervasive Positioning Standard states the data organization and defined formats for site signals and maps. A database would store the data provided by the site owners in the specified format. With this standard and the help of the site owners, users can have a more convenient experience with location applications.