VFGS.tex

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\begin{document}
\title{Visual Fidelity Guaranteed Sampling for \\ Large Trajectory Data Visualization}

%\author{Qiaomu Shen$^{*}$,\quad Chuan Yang$^{*}$,\quad Chaozu Zhang$^{*}$,\quad Dan Zeng$^{\dag}$, \quad Wei Zeng$^{\ddag}$, \quad Bo Tang$^{*}$}

\affiliation{
  \institution{Qiaomu Shen$^{\dag}$,\quad Chuan Yang$^{\dag}$,\quad Chaozu Zhang$^{\dag}$,\quad Dan Zeng$^{\ddag}$, \quad Wei Zeng$^{\$}$, \quad Bo Tang$^{\dag}$}
  \institution{$^{\dag}$ Department of Computer Science and Engineering, Southern University of Science and Technology}
  \mbox{qshen@connect.ust.hk, \{11612732@mail,11712021@mail,tangb3@\}sustech.edu.cn} \\
  \institution{$^{\ddag}$ Electrical Engineering, Mathematics and Computer Science, University of Twente}
  \institution{$^{\$}$ Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences}
  \mbox{d.zeng@utwente.nl, zengwei81@gmail.com}
  %\email{{{11711004, 11712532, 11613015}@mail.,tangb3@}sustech.edu.cn}
  %\mbox{qshen@connect.ust.hk, \{11612732@mail,11712021@mail,tangb3@\}sustech.edu.cn, d.zeng@utwente.nl, zengwei81@gmail.com}
}

\renewcommand{\shortauthors}{Qiaomu Shen, et al.}
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%% The abstract is a short summary of the work to be presented in the
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\begin{abstract}
Visualizing large-scale trajectory data is the core subroutine in many smart city applications, e.g., traffic management, route recommendation.
However, it suffers from limited rendering capability and visual clutter issues.
Sampling can effectively mitigate the issues, yet existing methods have an attenuating effect on visual fidelity.
In this work, we propose visual fidelity guaranteed sampling techniques for line-based visualization of large trajectories.
We first define a pixel-based fidelity loss function to capture the visual difference between two visualizations.
We prove that it is NP-hard to select a sized-$k$ subset of trajectories with minimal visual fidelity loss.
Next, we devise an approximation algorithm $\vats$ with a suite of optimization techniques, which returns fidelity-guaranteed visualizations efficiently.
Moreover, we propose $\avats$ which improves the effectiveness of $\vats$ by taking data distribution and human perception into consideration.
We conduct extensive experimental studies to demonstrate the effectiveness and efficiency of our methods on real-world trajectory datasets.
In addition, comprehensive user studies further illustrate the superiority of our proposals in various applications, e.g., traffic flow comparison, and reachable route inspection.
\end{abstract}

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\begingroup\small\noindent\raggedright\textbf{PVLDB Reference Format:}\\
\shortauthors. Visual Fidelity Guaranteed Sampling for Large Trajectory Data Visualization. PVLDB, \vldbvolume(\vldbissue): \vldbpages, \vldbyear.\\
\href{https://doi.org/\vldbdoi}{doi:\vldbdoi}
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\renewcommand\thefootnote{}\footnote{\noindent
This work is licensed under the Creative Commons BY-NC-ND 4.0 International License. Visit \url{https://creativecommons.org/licenses/by-nc-nd/4.0/} to view a copy of this license. For any use beyond those covered by this license, obtain permission by emailing \href{mailto:info@vldb.org}{info@vldb.org}. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment. \\
\raggedright Proceedings of the VLDB Endowment, Vol. \vldbvolume, No. \vldbissue\ %
ISSN 2150-8097. \\
\href{https://doi.org/\vldbdoi}{doi:\vldbdoi} \\
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\input{sections/Introduction.tex}

\input{sections/RelatedWork.tex}

\input{sections/ProblemFormulation.tex}

\input{sections/ProblemSolving.tex}

\input{sections/Evaluation.tex}

\input{sections/ConclusionFutureWork.tex}



%\begin{acks}
% This work was supported by the [...] Research Fund of [...] (Number [...]). Additional funding was provided by [...] and [...]. We also thank [...] for contributing [...].
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