Building Non-centralized Discovery of Big Trajectory Streams: A Hybrid System Design

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Trajectory streams consist of big volumes of time-stamped spatial data that are constantly generated from diverse and geographically distributed sources such as GPS positioning, sensors probing, mobile phones monitoring and many smart devices. Discovering patterns from trajectory streams has broad applications such as public emergency responses, point of interests, recommendation, health monitoring, safety regulation, fleet management and etc. To compute data mining or machine learning models, traditional methods adopt a centralized approach that requires the trajectory sampling data be aggregated on a single machine or in a datacenter. This centralized approach, however, has limitation on (1) scalable computing. The trajectory mining tasks demand on horizontal scaling of computing resources. Even if the trajectory dataset could be loaded in a centralized computing environment, the transformation over trajectory datasets among iterative steps of mining algorithms can generate data with much larger magnitude than the original. Another limitation is (2) privacy protection. Trajectory mining inevitably requires collection, transmission, and aggregation of trajectory data from de-centralized data sources. However, data should be shared without jeopardizing users privacy. Much work focuses on centralized setting where a trusted data curator located between the aggregator and data sources with perturbed noise to guarantee privacy. This centralized approach is vulnerable to attacks where an adversary may access the true unprotected data by attacking the trusted data curator. The long term goal of this research is defining non-centralized paradigm for collective sharing of trajectory data with privacy protection and scalable discovery of meaningful patterns. This research program has the short term goal in five years to investigate a hybrid system design that enables decentralized and collective releasing of trajectory streams with local privacy guarantee. This research focuses on three major research topics. Topic 1: Trajectory segmentation, feature representation and semi-supervised classification model in distributed and parallel computing environment. Topic 2: investigate collective trajectory data sharing with local differential. The research focuses on the budget allocation algorithms that require less information to be exchanged while retain the comparable utility. Topic 3: scaling the trajectory stream mining and reduce the end-to-end delay by means of improving data locality and rebalancing workload at runtime. Rebalancing the workload needs to assure the precision under parallelism of the new partition. Thanks to collaboration established with ICT companies in Canada including Ciena, Ubisoft, Neubla AI and Videotron, this research is able to derive experimental scenarios to validate assumptions and the system design from real-word cases.