School of Engineering Department of Computer Science and Engineering 95 AI Meets Big Data: User Analytics and Personalized Recommendation Based on Location Data Supervisor: CHAN Gary Shueng Han / CSE Student: XIA Zhiqiu / DSCT Course: UROP1100, Spring UROP2100, Summer This semester, I enrolled in UROP 2100 to continue my work on the project AI Meets Big Data: User Analytics and Personalized Recommendation Based on Location Data, supervised by Prof. Chan. In particular, I read the code of RoNIN carefully and reproduced its result smoothly. Based on that, Mr. He and me are building a system about personalized Pedestrian Dead Reckoning (PDR). This report mainly introduces the project background, project challenges, key observations, and several experiments with results. In the end, this report will briefly introduce our ideas and expectation for future work, which is left for UROP 3100. AI Meets Big Data: User Analytics and Personalized Recommendation Based on Location Data Supervisor: CHAN Gary Shueng Han / CSE Student: YAO Chongchong / DSCT Course: UROP1100, Spring 2D point-formed data models a lot of real problems and account for a large portion of realworld geographical data, on which a common and natural requirement is to perform range/region queries. To meet such requirements, an efficient algorithm for range querying should be proposed. R-tree is known as an indexing mechanism that can efficiently search non-zero size multi-dimensional data. In this project, I study the structure of R-tree, implement a modified version of R-tree that operates on 2D point-formed data, and test and visualize its performance on both randomly generated and specialized real-world data. Eventually, I come to the conclusion that R-tree is a powerful tool for handling range queries on point data when the search range is reasonable.
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