Calibrating trajectory data for similarity-based analysis

Su, Han; Zheng, Kai; Wang, Haozhou; Huang, Jiamin; Zhou, Xiaofang

doi:10.1145/2463676.2465303

Cited by 93 publications

(54 citation statements)

References 35 publications

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“…It is easy to observe that the two trajectories T A and T B are much more different than they are supposed to be. Such limitation has already been observed by a previous work [106]. Actually, the same route could result in very different raw trajectories under different sampling strategies, which thus leads to undesirably different summaries and are very hard for human to recognize.…”

Section: Problem Statementmentioning

confidence: 89%

“…This research [106] was published at the ACM International Conference on Management of Data (SIGMOD) 2013. The extended version [105] has been accepted by The International Journal on Very Large Data Bases (VLDBJ).…”

Section: Contributionsmentioning

confidence: 99%

“…The extended version [105] has been accepted by The International Journal on Very Large Data Bases (VLDBJ). We also presented efficient techniques to calibrate trajectories using both spatial and temporal information.…”

Section: Contributionsmentioning

confidence: 99%

“…In order to obtain the landmark-based route from a raw route, we employ our previous research results on anchor-based trajectory calibration [106] to rewrite the continuous recommend routes into landmark-based routes, by treating landmarks as anchor points. For example, …”

Section: Problem Statementmentioning

confidence: 99%

“…We employ our previous research results on anchor-based trajectory calibration [106] to rewrite the raw trajectory T into a landmark-based trajectory T , by treating landmarks as anchor points. The trajec- For a given symbolic trajectory…”

Section: Definition 52 (Landmark)mentioning

confidence: 99%

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Quality-aware trajectory processing using significant locations

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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...

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Section: Problem Statementmentioning

confidence: 89%

Section: Contributionsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Definition 52 (Landmark)mentioning

confidence: 99%

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