|4/2018 - 8|
Real-Time Clustering of Large Geo-Referenced Data for Visualizing on MapREZAEI, M. , FRANTI, P.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (3,681 KB) | Citation | Downloads: 125 | Views: 220|
data visualization, clustering methods, web services, client-server systems, Internet
visualization(21), data(17), clustering(13), information(12), graphics(8), tvcg(6), large(6), visual(5), mining(5), algorithm(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 63 - 74
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04008
Web of Science Accession Number: 000451843400008
SCOPUS ID: 85058811278
Displaying geo-referenced data in web mapping systems has become popular. However, most existing systems suffer from three annoying problems: (1) clutter when trying to visualize large amount of data; (2) slowness of transferring data over internet; (3) lack of support for dynamic queries. To solve these problems, we propose a real-time system using server-side clustering, transferring only the clustered data, and client-side visualization using existing map tools. As far as we know, there is no other scientific paper describing such real-time system that allows dynamic database queries without limiting to predefined queries. Experiments show that it can handle up to 1 million objects whereas all existing systems are either limited to pre-defined queries, or they support only a very small number of free parameters in the query whereas the proposed system has no such limitations.
|References|||||Cited By «-- Click to see who has cited this paper|
| M. Nollenburg, "Geographic visualization," in Human-centered visualization environments, pp. 257-294, 2007. |
 J. Delort, "Hierarchical cluster visualization in web mapping systems," 19th Int. Conf. World Wide Web, pp. 1241-1244, 2010.
[CrossRef] [SCOPUS Times Cited 10]
 J. K. Rayson, "Aggregate towers: Scale sensitive visualization and decluttering of geospatial data," IEEE Symposium on Information Visualization (Info Vis' 99), pp. 92-99, 1999.
 J. Korpi, P. Ahonen-Rainio, "Clutter reduction methods for point symbols in map mashups," The Cartographic Journal, vol. 50, no. 3, pp. 257-265, 2013.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 11]
 Z. Liu, B. Iiang, J. Heer, "imMens: Real-time visual querying of big data," Computer Graphics Forum, vol. 32, no. 3pt4, pp. 421-430, 2013.
[CrossRef] [Web of Science Times Cited 73] [SCOPUS Times Cited 122]
 J.-Y. Delort, "Vizualizing large spatial datasets in interactive maps," Advanced Geographic Information Systems, Applications, and Services (GEOPROCESSING), pp. 33-38, 2010.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 11]
 A. Jaffe, M. Naaman, T. Tassa, M. Davis, "Generating summaries and visualization for large collections of geo-referenced photographs," 8th ACM Int. Workshop on Multimedia Information Retrieval, pp. 89-98, 2006.
[CrossRef] [SCOPUS Times Cited 161]
 S. Ahern, M. Naaman, R. Nair, J. H. Yang, "World explorer: visualizing aggregate data from unstructured text in geo-referenced collections," 7th ACM/IEEE-CS Conf. Digital Libraries, pp. 1-10, 2007.
[CrossRef] [Web of Science Times Cited 73] [SCOPUS Times Cited 180]
 N. Elmqvist, J.-D. Fekete, "Hierarchical aggregation for information visualization: Overview, techniques, and design guidelines," IEEE Trans. on Visualization and Computer Graphics, vol. 16, no. 3, pp. 439-454, 2010.
[CrossRef] [Web of Science Times Cited 109] [SCOPUS Times Cited 180]
 I. Peca, H. Zhi, K. Vrotsou, N. Andrienko, G. Andrienko, "Kd-photomap: Exploring photographs in space and time," IEEE Conf. Visual Analytics Science and Technology (VAST), pp. 291-292, 2011.
[CrossRef] [SCOPUS Times Cited 5]
 M. Cristani, A. Perina, U. Castellani, V. Murino, "Content visualization and management of geo-located image databases," CHI'08 Extended Abstracts on Human Factors in Computing Systems, pp. 2823-2828, 2008.
[CrossRef] [SCOPUS Times Cited 9]
 F. Girardin, F. Calabrese, F. Dal Fiore, C. Ratti, J. Blat, "Digital footprinting: Uncovering tourists with user-generated content," IEEE Pervasive Computing, vol. 7, no. 4, 2008.
[CrossRef] [Web of Science Times Cited 139] [SCOPUS Times Cited 225]
 C. Lu, C. Chen, P. Cheng, "Clustering and visualizing geographic data using geo-tree," IEEE/WIC/ACM Int. Conf. Web Intelligence and Intelligent Agent Technology-Volume 01, pp. 479-482, 2011.
[CrossRef] [SCOPUS Times Cited 8]
 D. A. Keim, H. Kriegel, "VisDB: Database exploration using multidimensional visualization," IEEE Computer Graphics and Applications, vol. 14, no. 5, pp. 40-49, 1994.
[CrossRef] [Web of Science Times Cited 78] [SCOPUS Times Cited 172]
 F. H. Post, F. J. Post, T. Van Walsum, D. Silver, "Iconic techniques for feature visualization," 6th IEEE Conf. Visualization'95, p. 288, 1995.
[CrossRef] [Web of Science Times Cited 43]
 E. Keogh, L. Wei, X. Xi, S. Lonardi, J. Shieh, S. Sirowy, "Intelligent icons: Integrating lite-weight data mining and visualization into GUI operating systems," 6th Int. Conf. Data Mining, pp. 912-916, 2006.
[CrossRef] [SCOPUS Times Cited 25]
 N. Cao, D. Gotz, J. Sun, H. Qu, "Dicon: Interactive visual analysis of multidimensional clusters," IEEE Trans. on Visualization and Computer Graphics, vol. 17, no. 12, pp. 2581-2590, 2011.
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 58]
 D. Fisher, "Hotmap: Looking at geographic attention," IEEE Trans. on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1184-1191, 2007.
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 87]
 A. Mayorga, M. Gleicher, "Splatterplots: Overcoming overdraw in scatter plots," IEEE Trans. on Visualization and Computer Graphics, vol. 19, no. 9, pp. 1526-1538, 2013.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 42]
 B. Shneiderman, "The eyes have it: A task by data type taxonomy for information visualizations," IEEE Symposium on Visual Languages, pp. 336-343, 1996.
[CrossRef] [Web of Science Times Cited 991]
 G. Ellis, A. Dix, "A taxonomy of clutter reduction for information visualisation," IEEE Trans. on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1216-1223, 2007.
[CrossRef] [Web of Science Times Cited 139] [SCOPUS Times Cited 199]
 J.-D. Fekete, C. Plaisant, "Interactive information visualization of a million items," IEEE Symposium on Information Visualization, INFOVIS, pp. 117-124, 2002.
[CrossRef] [Web of Science Times Cited 45] [SCOPUS Times Cited 137]
 W. Wang, J. Yang, R. Muntz, "STING: A statistical information grid approach to spatial data mining," VLDB, vol. 97, pp. 186-195, 1997.
 R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan, "Automatic subspace clustering of high dimensional data for data mining applications," ACM SIGMOD Int. Conf. Management of Data, vol. 27, no. 2, pp. 94-105, 1998.
 J. Dabernig, "Geocluster: server-side clustering for mapping in Drupal based on Geohash," M.Sc. Thesis, Faculty of Informatics, TU Wien University, Austria, 2013.
 L. Lins, J. T. Klosowski, C. Scheidegger, "Nanocubes for real-time exploration of spatiotemporal datasets," IEEE Trans. on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2456-2465, 2013.
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 88]
 D. Nguyen, H. Schumann, "Taggram: Exploring geo-data on maps through a tag cloud-based visualization," 14th Int. Conf. Information Visualisation (IV), pp. 322-328, 2010.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 16]
 R. T. Ng, J. Han, "CLARANS: A method for clustering objects for spatial data mining," IEEE Trans. on Knowledge and Data Engineering, vol. 14, no. 5, pp. 1003-1016, 2002.
[CrossRef] [Web of Science Times Cited 335] [SCOPUS Times Cited 514]
 D. R. Edla, P. K. Jana, "A grid clustering algorithm using cluster boundaries," World Congress on Information and Communication Technologies (WICT), pp. 254-259, 2012.
[CrossRef] [SCOPUS Times Cited 5]
 S. Na, L. Xumin, G. Yong, "Research on k-means clustering algorithm: An improved k-means clustering algorithm," Intelligent Information Technology and Security Informatics (IITSI), pp. 63-67, 2010.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 130]
 M. Ester, H.-P. Kriegel, J. Sander, X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," KDD, vol. 96, no. 34, pp. 226-231, 1996.
 B. Liu, "A fast density-based clustering algorithm for large databases," Int. Conf. Machine Learning and Cybernetics, pp. 996-1000, 2006.
[CrossRef] [SCOPUS Times Cited 24]
 L. Zhao, J. Yang, J. Fan, "A fast method of coarse density clustering for large data sets," 2nd Int. Conf. Biomedical Engineering and Informatics, BMEI'09, pp. 1-5, 2009.
[CrossRef] [SCOPUS Times Cited 2]
 P. Franti, O. Virmajoki, V. Hautamaki, "Fast agglomerative clustering using a k-nearest neighbor graph," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp. 1875-1881, 2006.
[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 112]
 P. Franti, T. Kaukoranta, D. Shen, K. Chang, "Fast and memory efficient implementation of the exact PNN," IEEE Trans. on Image Processing, vol. 9, no. 5, pp. 773-777, 2000.
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 45]
 M. Steinbach, L. Ertoz, V. Kumar, "The challenges of clustering high dimensional data," New Directions in Statistical Physics: Springer, pp. 273-309, 2004.
 J. H. Ward Jr, "Hierarchical grouping to optimize an objective function," J. American Statistical Association, vol. 58, no. 301, pp. 236-244, 1963.
[CrossRef] [SCOPUS Times Cited 9051]
 W. Meert, "Clustering maps," M.Sc. Thesis, Faculty of Engineering, University of Leuven, Belgium, 2006.
 T. Zhang, R. Ramakrishnan, M. Livny, "BIRCH: an efficient data clustering method for very large databases," ACM Sigmod Record, vol. 25, no. 2, pp. 103-114, 1996.
Web of Science® Citations for all references: 2,386 TCR
SCOPUS® Citations for all references: 11,629 TCR
Web of Science® Average Citations per reference: 60 ACR
SCOPUS® Average Citations per reference: 291 ACR
TCR = Total Citations for References / ACR = Average Citations per Reference
We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more
Citations for references updated on 2019-03-17 20:53 in 251 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.