Ontology-Based Heterogeneous Data Integration Technology

Kang Li; Xin Ming Li; Dong  Liu; Yun Fei Cui

doi:10.4028/www.scientific.net/AMM.380-384.3900

Paper Titles

Design of Low-Noise, High-Density Signal Intelligent Detection System
p.3882

An Advanced Method for Human Body Detection
p.3886

Multi-Layer and Adaptive Edge Detection Method Based on Multi-Scale Gabor Wavelets
p.3890

Fusion of Background Subtraction and Frame Difference in HSV Space for Multi-Object Detection
p.3895

Ontology-Based Heterogeneous Data Integration Technology
p.3900

Detection and Reconstruction for LFM Echo Signal Based on Blind Compressed Sensing
p.3904

Attenuation Model of Antenna Signal with Barriers in Wireless Sensor Network
p.3908

Research on RACH Signal Detection Algorithm in TD-LTE System
p.3912

Face Detection System Based on AdaBoost Algorithm
p.3917

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384Ontology-Based Heterogeneous Data Integration...

Ontology-Based Heterogeneous Data Integration Technology

Abstract:

For the vast amounts of heterogeneous data is difficult to effectively deal with the problem, propose an ontology-based data integration technology. Analysis of the types of semantic conflicts in data integration, ontology semantic description of traditional data integration framework for data integration middleware module. Ontology concept in the domain of information resources, and take the initiative to find and build semantic mapping between semantic conflicts.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

3900-3903

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.3900

Citation:

Cite this paper

Online since:

August 2013

Authors:

Kang Li, Xin Ming Li, Dong Liu, Yun Fei Cui

Keywords:

Data Integration, Heterogeneous, Ontology

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Kenneth Cukier. Data, data everywhere. Economist, (2010).

Google Scholar

[2] John F. Gantz, et al. The diverse and exploding digital universe: an updated forecast of worldwide information growth through 2011. An IDC White Paper, (2008).

Google Scholar

[3] Cory Doctorow. Big data: Welcome to the petacentre. Nature, Vol. 455, No. 7209, (2008).

DOI: 10.1038/455016a

Google Scholar

[4] Jeffrey Cohen, Brian Dolan, Mark Dunlap, Joseph M. Hellerstein, and Caleb Welton. MAD skills: new analysis practices for big data. PVLDB, 2009. 2(2): 1481～1492.

DOI: 10.14778/1687553.1687576

Google Scholar

[5] Cruz IF, Xiao H. The role of ontologies in data integration. Engineering intelligent systems for electrical engineering and communications, 2005, 13(4): 245.

Google Scholar

[6] Pires CES, Santiago RML, Salgado AC etc. Ontology-based Clustering in a Peer Data Management System. International Journal of Distributed Systems and Technologies (IJDST), 2012, 3(2): 1-21.

DOI: 10.4018/jdst.2012040101

Google Scholar

[7] D. M Huang, Y. L Du, M. H Zhang etc. Application of ontology-based automatic ETL in marine data integration; proceedings of the Electrical & Electronics Engineering (EEESYM), 2012 IEEE Symposium on, F, 2012. IEEE.

DOI: 10.1109/eeesym.2012.6258574

Google Scholar

[8] Buccella A, Cechich A, Brisaboa NR. An ontology approach to data integration. Journal of computer science and technology, 2003, 3(2): 62-68.

Google Scholar