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JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Aug 2024
Next issue: Nov 2024
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PUBLISHER

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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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  4/2018 - 8

 HIGHLY CITED PAPER 

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
 
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Download PDF pdficon (3,681 KB) | Citation | Downloads: 1,018 | Views: 3,207

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.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 5
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Cited-By CrossRef

[1] Visualizing Imitation of Typical Intense X-ray Radiation Processes, LIU, H., YANG, S., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 20, 2020.
Digital Object Identifier: 10.4316/AECE.2020.01003
[CrossRef] [Full text]

[2] La géovisualisation de données massives sur le Web : entre avancées technologiques et évolutions cartographiques, Mericskay, Boris, Mappemonde, ISSN 0764-3470, Volume , 2021.
Digital Object Identifier: 10.4000/mappemonde.5595
[CrossRef]

[3] Activity event recommendation and attendance prediction, Mariescu-Istodor, Radu, Sayem, Abu S. M., Fränti, Pasi, Journal of Location Based Services, ISSN 1748-9725, Issue 4, Volume 13, 2019.
Digital Object Identifier: 10.1080/17489725.2019.1660423
[CrossRef]

[4] Solutions and approaches analysis for geospatial data clustering to optimize performance and user experience of web maps, Arzubov, M. V., Batyuk, A. Ye., Ukrainian Journal of Information Technology, ISSN 2707-1898, Issue 2, Volume 5, 2023.
Digital Object Identifier: 10.23939/ujit2023.02.088
[CrossRef]

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