Research on Visualization of Multi-Dimensional Real-Time Traffic Data Stream Based on Cloud Computing

Based on efficient continuous parallel query series algorithm supporting multi-objective optimization, by using visual graphics technology for traffic data streams for efficient real-time graphical visualization, it improve human-computer interaction, to realize real-time and visual data analysis and to improve efficiency and accuracy of the analysis. This paper employs data mining processing and statistical analysis on real-time traffic data stream, based on the parameters standards of various data mining algorithms, and by using computer graphics and image processing technology, converts graphics or images and make them displayed on the screen according to the system requirements, in order to track, forecast and maintain the operating condition of all traffic service systems effectively.

[1]  Lloyd D Bennett,et al.  3D Visualization of Traffic-Induced Air Pollution Impacts of Urban Transport Schemes , 2013 .

[2]  Dinesh Manocha,et al.  Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatio-Temporal Data , 2009, 2009 IEEE Virtual Reality Conference.

[3]  Xu Ben,et al.  Mining Developer Contribution in Open Source Software Using Visualization Techniques , 2013, 2013 Third International Conference on Intelligent System Design and Engineering Applications.

[4]  A. Shahrestani,et al.  Visualization of invariant bot behavior for effective botnet traffic detection , 2012, 2012 International Symposium on Telecommunication Technologies.

[5]  A. M. Adeshina,et al.  Multimodal 3-D reconstruction of human anatomical structures using surlens visualization system , 2013, Interdisciplinary Sciences: Computational Life Sciences.

[6]  Golan Yona,et al.  Distributional Scaling: An Algorithm for Structure-Preserving Embedding of Metric and Nonmetric Spaces , 2004, J. Mach. Learn. Res..

[7]  K. Nakamoto,et al.  Gamma-ray visualization module , 2013, 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC).

[8]  Shu-Lin Wang,et al.  Fast ISOMAP Based on Minimum Set Coverage , 2010, ICIC.

[9]  Peter Brusilovsky,et al.  Adaptive visualization for exploratory information retrieval , 2013, Inf. Process. Manag..

[10]  Susan R. Fussell,et al.  Effects of visualization and note-taking on sensemaking and analysis , 2013, CHI.

[11]  Zibin Zheng,et al.  Personalized QoS-Aware Web Service Recommendation and Visualization , 2013, IEEE Transactions on Services Computing.

[12]  Daniel Gonçalves,et al.  Studying Color Blending Perception for Data Visualization , 2014, EuroVis.

[13]  M. Sheelagh T. Carpendale,et al.  Empirical Studies in Information Visualization: Seven Scenarios , 2012, IEEE Transactions on Visualization and Computer Graphics.

[14]  Xiaoyuan He,et al.  A Wargame Data Visualization Algorithm Based on Regular Radius and Constrained Random Direction , 2014 .

[15]  Valentin Patilea,et al.  Breaking the curse of dimensionality in nonparametric testing , 2008 .

[16]  Harvey J. Miller,et al.  Exploring traffic flow databases using space-time plots and data cubes , 2011, Transportation.

[17]  Helwig Hauser,et al.  Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey , 2013, IEEE Transactions on Visualization and Computer Graphics.

[18]  Mohan M. Trivedi,et al.  Real-Time Video-Based Traffic Measurement and Visualization System for Energy/Emissions , 2012, IEEE Transactions on Intelligent Transportation Systems.

[19]  Cristina Conati,et al.  Individual user characteristics and information visualization: connecting the dots through eye tracking , 2013, CHI.

[20]  W G Reed A SIMPLE METHOD OF INDICATING GEOGRAPHICAL DISTRIBUTION. , 1915, Science.

[21]  Abdullah Al Mamun,et al.  Weighted locally linear embedding for dimension reduction , 2009, Pattern Recognit..

[22]  Nabeel Khwaja,et al.  An Integrated Visualization Technique for Transportation Management Planning in Highway Infrastructure Projects , 2015 .

[23]  Habtom W. Ressom,et al.  Adaptive double self-organizing maps for clustering gene expression profiles , 2003, Neural Networks.

[24]  Robert J. K. Jacob,et al.  Using fNIRS brain sensing to evaluate information visualization interfaces , 2013, CHI.

[25]  Dinesh Manocha,et al.  Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatio-Temporal Data , 2009, VR.

[26]  Na Li,et al.  Traffic flow visualization based on line integral convolution , 2014, Other Conferences.

[27]  Shao Chao and Huang Houkuan A New Data Visualization Algorithm Based on ISOMAP , 2006 .

[28]  Álvaro Herrero,et al.  Neural visualization of network traffic data for intrusion detection , 2011, Appl. Soft Comput..

[29]  Enrico Bertini,et al.  Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization , 2011, IEEE Transactions on Visualization and Computer Graphics.