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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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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.

Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

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  3/2015 - 18

HiGIS: An Open Framework for High Performance Geographic Information System

XIONG, W. See more information about XIONG, W. on SCOPUS See more information about XIONG, W. on IEEExplore See more information about XIONG, W. on Web of Science, CHEN, L. See more information about CHEN, L. on SCOPUS See more information about CHEN, L. on SCOPUS See more information about CHEN, L. 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 (1,702 KB) | Citation | Downloads: 329 | Views: 2,089

Author keywords
high performance computing, geographic information system, geocomputation, communicating sequential process

References keywords
parallel(10), computing(8), cloud(7), system(6), data(6), processing(5), geospatial(5), remote(4), performance(4), high(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-08-31
Volume 15, Issue 3, Year 2015, On page(s): 123 - 132
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.03018
Web of Science Accession Number: 000360171500018
SCOPUS ID: 84940739658

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/Big data/ era expose many challenges to geospatial data management, geocomputation and cartography. There is no exception in geographic information systems (GIS) community. Technologies and facilities of high performance computing (HPC) become more and more feasible to researchers, while mobile computing, ubiquitous computing, and cloud computing are emerging. But traditional GIS need to be improved to take advantages of all these evolutions. We proposed and implemented a GIS married with high performance computing, which is called HiGIS. The goal of HiGIS is to promote the performance of geocomputation by leveraging the power of HPC, and to build an open framework for geospatial data storing, processing, displaying and sharing. In this paper the architecture, data model and modules of the HiGIS system are introduced. A geocomputation scheduling engine based on communicating sequential process was designed to exploit spatial analysis and processing. Parallel I/O strategy using file view was proposed to improve the performance of geospatial raster data access. In order to support web-based online mapping, an interactive cartographic script was provided to represent a map. A demostration of locating house was used to manifest the characteristics of HiGIS. Parallel and concurrency performance experiments show the feasibility of this system.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 946 TCR
SCOPUS® Citations for all references: 1,617 TCR

Web of Science® Average Citations per reference: 39 ACR
SCOPUS® Average Citations per reference: 67 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 2019-06-16 09:53 in 134 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.

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

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