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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

2018-Jun-27
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  4/2018 - 8

Real-Time Clustering of Large Geo-Referenced Data for Visualizing on Map

REZAEI, M. See more information about REZAEI, M. on SCOPUS See more information about REZAEI, M. on IEEExplore See more information about REZAEI, M. on Web of Science, FRANTI, P. See more information about FRANTI, P. on SCOPUS See more information about FRANTI, P. on SCOPUS See more information about FRANTI, P. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (3,681 KB) | Citation | Downloads: 243 | Views: 461

Author keywords
data visualization, clustering methods, web services, client-server systems, Internet

References keywords
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

Abstract
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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.


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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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