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Streaming Data Algorithm Design for Big Trajectory Data Analysis


Streaming Data Algorithm Design for Big Trajectory Data Analysis

Xian, Yong Yi (2017) Streaming Data Algorithm Design for Big Trajectory Data Analysis. Masters thesis, Concordia University.

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Trajectory streams consist of large volumes of time-stamped spatial data that are constantly generated from diverse and geographically distributed sources. Discovery of traveling patterns on trajectorystreamssuchasgatheringandcompaniesneedstoprocesseachrecordwhenitarrivesand correlatesacrossmultiplerecordsnearreal-time. Thustechniquesforhandlinghigh-speedtrajectorystreamsshouldscaleondistributedclustercomputing. Themainissuesencapsulatethreeaspects, namely a data model to represent the continuous trajectory data, the parallelism of a discovery algorithm, and end-to-end performance improvement. In this thesis, I propose two parallel discovery methods,namelysnapshotmodelandslotmodelthateachconsistsof1)amodelofpartitioningtrajectoriessampledondifferenttimeintervals;2)definitionondistancemeasurementsoftrajectories; and 3) a parallel discovery algorithm. I develop these methods in a stream processing workflow. I evaluate our solution with a public dataset on Amazon Web Services (AWS) cloud cluster. From parallelization point of view, I investigate system performance, scalability, stability and pinpoint principle operations that contribute most to the run-time cost of computation and data shuffling. I improve data locality with fine-tuned data partition and data aggregation techniques. I observe that both models can scale on a cluster of nodes as the intensity of trajectory data streams grows. Generally, snapshot model has higher throughput thus lower latency, while slot model produce more accurate trajectory discovery.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Xian, Yong Yi
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:16 June 2017
Thesis Supervisor(s):Liu, Yan
ID Code:982628
Deposited By: YONG YI XIAN
Deposited On:10 Nov 2017 15:48
Last Modified:18 Jan 2018 17:55
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