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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 75 days
Avg accept to publ: 48 days
APC: 300 EUR


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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LATEST NEWS

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.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

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  1/2012 - 8

Enabling Semantic Queries Against the Spatial Database

PENG, X. See more information about PENG, X. on SCOPUS See more information about PENG, X. on IEEExplore See more information about PENG, X. on Web of Science, HUANG, Z. See more information about HUANG, Z. on SCOPUS See more information about HUANG, Z. on SCOPUS See more information about HUANG, Z. on Web of Science
 
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Download PDF pdficon (690 KB) | Citation | Downloads: 1,255 | Views: 4,175

Author keywords
spatial database, semantic web, query processing

References keywords
No relevant keywords could be extracted from the references.

About this article
Date of Publication: 2012-02-28
Volume 12, Issue 1, Year 2012, On page(s): 45 - 50
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.01008
Web of Science Accession Number: 000301075000008
SCOPUS ID: 84860743212

Abstract
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The spatial database based upon the object-relational database management system (ORDBMS) has the merits of a clear data model, good operability and high query efficiency. That is why it has been widely used in spatial data organization and management. However, it cannot express the semantic relationships among geospatial objects, making the query results difficult to meet the user's requirement well. Therefore, this paper represents an attempt to combine the Semantic Web technology with the spatial database so as to make up for the traditional database's disadvantages. In this way, on the one hand, users can take advantages of ORDBMS to store and manage spatial data; on the other hand, if the spatial database is released in the form of Semantic Web, the users could describe a query more concisely with the cognitive pattern which is similar to that of daily life. As a consequence, this methodology enables the benefits of both Semantic Web and the object-relational database (ORDB) available. The paper discusses systematically the semantic enriched spatial database's architecture, key technologies and implementation. Subsequently, we demonstrate the function of spatial semantic queries via a practical prototype system. The query results indicate that the method used in this study is feasible.


References | Cited By

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Cited-By SCOPUS

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Cited-By CrossRef

[1] COMPARISON OF THE DATA MATCHING PERFORMANCES OF STRING SIMILARITY ALGORITHMS IN BIG DATA, AKSOY, Bekir, UĞUZ, Sinan, ORAL, Okan, Mühendislik Bilimleri ve Tasarım Dergisi, ISSN 1308-6693, Issue 3, Volume 7, 2019.
Digital Object Identifier: 10.21923/jesd.467036
[CrossRef]

[2] Post-processing of Deep Web Information Extraction Based on Domain Ontology, LIU, L., PENG, T., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 13, 2013.
Digital Object Identifier: 10.4316/AECE.2013.04005
[CrossRef] [Full text]

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


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