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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Aug 2017
Next issue: Nov 2017
Avg review time: 75 days


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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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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
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.

2017-Jan-30
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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  3/2009 - 2

GEOBARN: A Practical Grid Geospatial Database System

HUANG, Z. See more information about HUANG, Z. on SCOPUS See more information about HUANG, Z. on IEEExplore See more information about HUANG, Z. on Web of Science, FANG, Y. See more information about  FANG, Y. on SCOPUS See more information about  FANG, Y. on SCOPUS See more information about FANG, Y. on Web of Science, XIE, X. See more information about  XIE, X. on SCOPUS See more information about  XIE, X. on SCOPUS See more information about XIE, X. on Web of Science, PAN, M. See more information about PAN, M. on SCOPUS See more information about PAN, M. on SCOPUS See more information about PAN, M. on Web of Science
 
Click to see author's profile on 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 (939 KB) | Citation | Downloads: 1,100 | Views: 3,633

Author keywords
distributed database, grid computing, GIS

References keywords
data(12), spatial(6), information(6), geospatial(6), system(4), knowledge(4), distributed(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2009-10-26
Volume 9, Issue 3, Year 2009, On page(s): 7 - 11
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.03002
Web of Science Accession Number: 000271872000002
SCOPUS ID: 77954684261

Abstract
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Full text preview
Recently, more and more geospatial data are generated and distributed with the promising data acquirement techniques like satellite imaging and digital cartography. So, it is an important issue to effectively make use of these huge geospatial data resources. This means that effective data processing technologies need to be developed to support distributed query and analysis operations onto the distributed resources. The emergence of grid computing technology offers a good solution to accomplish this goal. To combine the grid computing technique with geospatial database we design and implement a GGDS (Grid Geospatial Database System) prototype named GEOBARN. Key modules and detailed implementation methods of GEOBARN are discussed in this paper. By using the GEOBARN system, both reliable data management and effective distributed geospatial query can be achieved.


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

[1] M. Erwig, M. Schneider and R.H. Guting, "Temporal Objects for Geospatial Data Models and a Comparison of Their Representations", Proceedings of the Workshops on Data Warehousing and Data Mining (1998)

[2] Y. Xue, A.P. Cracknell and H.D. Guo, "Telegeoprocessing: The integration of remote sensing, geographic information system (GIS), global positioning system (GPS) and telecommunication", International Journal of Remote Sensing. 23: 1851-1893 (2002)
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 33]


[3] Z. Shen, J. Luo and C. Zhou, "Architecture design of grid GIS and its applications on image processing based on LAN", Information Sciences. 166:1-17 (2004)
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 44]


[4] I. Foster, C. Kesselman, J.M. Nick and S. Tuecke "Grid services for distributed system integration", Computer. 6:37-46 (2002)
[CrossRef] [Web of Science Times Cited 455] [SCOPUS Times Cited 940]


[5] "The Globus Project Argonne National Laboratory USC Information Sciences Institute: Grid Architecture", Available from: http://www.globus.org

[6] N. Giannadakis, A. Rowe, M. Ghanem, Y. K. Guo, "InfoGrid: providing information integration for knowledge discovery", Information Sciences. 155:199-226 (2003)
[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 27]


[7] C.C. Aggarwal, "A framework for diagnosing changes in evolving data streams", Proceedings of the 2003 ACM SIGMOD international conference on Management of data (2003)
[CrossRef]


[8] H. Cao, O. Wolfson and G. Trajcevski, "Geospatial data reduction with deterministic error bounds", Proceedings of the 2003 joint workshop on Foundations of mobile computing (2003)

[9] M. J. Egenhofer, "Spatial SQL, A query and presentation language", IEEE Transactions on Knowledge and Data Engineering. 6(1): 86-95 (1994)
[CrossRef] [Web of Science Times Cited 139] [SCOPUS Times Cited 203]


[10] S.K. Harms, J. Deogun, S. Goddard, "Building knowledge discovery into a geo-spatial decision support system", Proceedings of the 2003 ACM symposium on Applied computing (2003)
[CrossRef]


[11] C.R. Lin, K.H. Liu, M.S. Chen, "Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains", IEEE Transactions on Knowledge and Data Engineering. 17(5): 39-48 (2005)
[CrossRef] [Web of Science Times Cited 26]


[12] K. Hornsby, "Workshop Report - International Workshop on Temporal, Spatial, and Geospatial Data Mining", Proceedings of the First International Workshop on Temporal, Spatial, and Geospatial Data Mining (2000)

[13] Z. Huang, Y. Fang, B. Chen, D. Yin and X. Peng, "A Global View Oriented Approach to Directory Management in Distributed Spatial Database", Proceedings of SPIE-The International Society for Optical Engineering, Vol.6418, GNSS and Integrated Geospatial Applications. 64181O (2006)
[CrossRef] [Web of Science Record] [SCOPUS Times Cited 3]


[14] L. Savary, T. Wan and K. Zeitouni, "Geospatial Data Warehouse Design for Human Activity Pattern Analysis", Proceedings of the Database and Expert Systems Applications, 15th International Workshop (2004)

[15] Z. Huang, B. Chen and Y. Fang, "An SQL Transformation Based Optimization Algorithm of Distributed Spatial Query", High Technology Letters. 17(10): 1013-1018(2007)

[16] Z. Huang, X. Peng and K. Zhang. "An Approach to Geographic SQL Global Parsing under Distributed Computation Circumstance", Geography and Geographic Information Science. 21(3): 18-21(2006)



References Weight

Web of Science® Citations for all references: 684 TCR
SCOPUS® Citations for all references: 1,250 TCR

Web of Science® Average Citations per reference: 40 ACR
SCOPUS® Average Citations per reference: 74 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 2017-09-19 21:38 in 65 seconds.




Note1: Web of Science® is a registered trademark of Thomson Reuters.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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