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Real-Time Clustering of Large Geo-Referenced Data for Visualizing on MapREZAEI, M. , FRANTI, P.
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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)
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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 10] [SCOPUS Times Cited 13]
 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 76] [SCOPUS Times Cited 125]
 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 164]
 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 74] [SCOPUS Times Cited 181]
 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 113] [SCOPUS Times Cited 182]
 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 143] [SCOPUS Times Cited 229]
 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 79] [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 45]
 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 49] [SCOPUS Times Cited 59]
 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 39] [SCOPUS Times Cited 44]
 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 1071]
 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 145] [SCOPUS Times Cited 208]
 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 138]
 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 70] [SCOPUS Times Cited 90]
 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 6] [SCOPUS Times Cited 17]
 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 349] [SCOPUS Times Cited 534]
 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 37] [SCOPUS Times Cited 136]
 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 25]
 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 84] [SCOPUS Times Cited 119]
 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 47]
 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 9232]
 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.
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