Quality-aware trajectory processing using significant locations

Driven by major advances in sensor technology, GPS-enabled mobile devices and wireless communications, a large amount of trajectory data is currently generated and managed in scores of application domains. This inspires a tremendous amount of research effort analyzing large scale trajectory data from a variety of perspectives in the last decade. However, people are still witnessing that data quality issues still persist in trajectory data and various kinds of trajectory-based services, mainly at 3 different levels: (1) the data level, e.g., heterogeneous and uncertain trajectory data; (2) the service level, e.g., the inability of capturing latent factors behind trajectory data; and (3) the representation level, e.g., the lack of semantic meaning in existing representation techniques of trajectory data. Such quality issues can be tied to the process and limited techniques that generate trajectory data, and the way that trajectory data is stored and presented. In this thesis , we tackle these quality issues in a systematic way using sampling of significant locations from all three levels. Below is a brief description of our contributions: • Data Level We pioneer a systematic approach to trajectory calibration that is a process to transform a heterogeneous trajectory dataset into one with (almost) unified sampling strategies. Trajectories in a practical database are always heterogeneous since a trajectory is a discrete approximation of the original continuous path, created by sampling the locations periodically, thus different sampling strategies result in a set of heterogeneous trajectory data. The heterogeneity of trajectory data has a negative impact on the effectiveness of similarity-based trajectory analysis, which is the foundation of most trajectory data processing tasks. Our solution was to take two steps for calibration: 1) the first step is to align the raw trajectories to a set of significant locations; 2) the second step is to interpolate several missing significant locations into the aligned trajectory. We have conducted extensive experiments based on a large-scale real trajectory dataset, which empirically demonstrates that the calibration system can significantly improve the effectiveness of most popular similarity measures for heterogeneous trajectories. • Service Level We propose the CrowdPlanner – a crowd-based route recommendation system, which requests human workers to evaluate candidate routes recommended by different sources and methods, and this determines the best route based on their feedback[Feedback is an uncountable noun]. The route recommendation system is one of the most important trajectorybased applications. The routes recommended by the big-thumb service providers try to give users the best traveling experience according to criteria, such as traveling distance, traveling time, traffic condition, etc. However, previous research shows that even the routes recommended by the big-thumb service providers can deviate significantly from the routes traveled by experienced drivers. This then means that travelers’ preferences on route selection are influenced by many latent and dynamic factors that are hard to model exactly with pre-defined formulas. So CrowdPlanner is used to leverage crowds’ knowledge to improve the recommendation quality. In this system, two important components that affect system performance significantly are well designed: 1) the task generation component to efficiently generate tasks which are simple to answer; and (2) the worker selection component to quickly identify a set of appropriate domain experts to answer the questions in a timely and accurate way. We deployed the system and conducted extensive experiments with several workers, users and queries in real scenarios. The results demonstrate that CrowdPlanner can recommend the most satisfactory routes efficiently in most cases. • Representation Level We generate a short text to enhance the semantic meaning of the trajectory. A raw trajectory in the form of a sequence of timestamped locations does not make much sense for humans without semantic representation. So we have aimed to facilitate human’s understanding of a raw trajectory by automatically generating a short text to describe it. By formulating this task as a problem of adaptive trajectory segmentation and feature selection, we propose a partition-and-summarization framework. In the partition phase, we first define a set of features for each trajectory segment and then derive an optimal partition, with the aim of making the segments within each partition as homogeneous as possible in terms of their features. In the summarization phase, for each partition, we select the most interesting features by comparing against the common behaviors of historical trajectories on the same route and we generate short text descriptions for these features. Comprehensive experiments were conducted, which empirically demonstrates that the generated textual descriptions can reflect the most significant features of trajectories and are easier for humans to understand. Declaration by Author This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis. I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award. I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School. I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis. Publications during candidature Journal paper: • Han Su, Kai Zheng, Jiamin Huang, Haozhou Wang and Xiaofang Zhou. Calibrating Trajectory Data for Spatio-temporal Similarity Analysis. The VLDB Journal (Accepted). Conference papers: • Han Su, Kai Zheng, Kai Zeng, Jiamin Huang, Nicholas Jing Yuan, Xiaofang Zhou. Making Sense of Trajectory Data: A Partition-and-Summarization Approach. ICDE 2015, Seoul. (Accepted) • Kai Zheng, Han Su, Bolong Zheng, Jiajun Liu, Xiaofang Zhou. Interactive Top-k Spatial Keyword Queries. ICDE 2015, Seoul. (Accepted) • Han Su, Kai Zheng, Jiamin Huang, Hoyoung Jeung, Lei Chen, Xiaofang Zhou. CrowdPlanner: A Crowd-Based Route Recommendation System. ICDE 2014, Chicago, 1144-1155. • Haozhou Wang, Kai Zheng, Han Su, Jiping Wang, Shazia Sadiq, Xiaofang Zhou: Efficient Aggregate Farthest Neighbor Query Processing on Road Networks. ADC 2014, Brisbane, 1325. • Han Su, Kai Zheng, Haozhou Wang, Jiamin Huang, Xiaofang Zhou. Calibrating Trajectory Data for Similarity-based Analysis. SIGMOD 2013, New York, 833-844. • Haozhou Wang, Han Su, Kai Zheng, Shazia Sadiq, Xiaofang Zhou. An Effectiveness Study on Trajectory Similarity Measures. ADC 2013, Adelaide, 13-22. (Best Paper Award). Demostration papers: • Han Su, Kai Zheng, Kai Zeng, Jiamin Huang and Xiaofang Zhou. STMaker A System to Make Sense of Trajectory Data. PVLDB 2014, Hangzhou, 1701-1704. • Han Su, Kai Zheng, Jiamin Huang, Tianyu Liu, Haozhou Wang, Xiaofang Zhou.A CrowdBased Route Recommendation System–CrowdPlanner. ICDE 2014, Chicago, 1178-1181. Publications included in this thesis Han Su, Kai Zheng, Haozhou Wang, Jiamin Huang, Xiaofang Zhou. Calibrating Trajectory Data for Similarity-based Analysis. SIGMOD 2013, New York, 833-844. incorporated as Chapter 3. Contributor Statement of contribution Han Su (Candidate) Designed algorithm and experiments (60%) Wrote the paper (60%) Kai Zheng Wrote the paper (40%) Discussion and analysis of the algorithm design Haozhou Wang Discussion and analysis of the algorithm design Jiamin Huang Designed algorithm and experiments (40%) Xiaofang Zhou Proof reading for the paper Discussion and analysis of the algorithm design Han Su, Kai Zheng, Jiamin Huang, Hoyoung Jeung, Lei Chen, Xiaofang Zhou. CrowdPlanner: A Crowd-Based Route Recommendation System. ICDE 2014, Chicago, 1144-1155. incorporated as Chapter 4. Han Su, Kai Zheng, Kai Zeng, Jiamin Huang, Nicholas Jing Yuan, Xiaofang Zhou. Contributor Statement of contribution Han Su (Candidate) Designed algorithms and experiments (60%) Wrote the paper (50%) Kai Zheng Wrote the paper (30%) Discussion and analysis of the algorithm design Jiamin Huang Designed experiments (40%) Hoyoung Jeung Wrote the paper(10%) Discussion and analysis of the algorithm design Lei Chen Wrote the paper(10%) Discussion and analysis of the algorithm design Xiaofang Zhou Proof reading for the paper Discussion and analysis of the algorithm design Making Sense of Trajectory Data: A Partition-and-Summarization Approach. ICDE 2015, Seoul. incorporated as Chapter 5. Contributor Statement of contribution Han Su (Candidate) Designed algorithms and experiments (50%) Wrote the paper (60%) Kai Zheng Wrote the paper (30%) Kai Zeng Wrote the paper (10%) Designed algorithms and experiments (10%) Jiamin Huang Designed algorithms and experiments (40%) Nicholas J