On-Demand Service-Based Big Data Integration: Optimized for Research Collaboration-Reference-Cited by-同舟云学术

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On-Demand Service-Based Big Data Integration: Optimized for Research Collaboration

Published:2017 Issue: Volume: Page:9-28
ISSN:0302-9743
Container-title:Data Management and Analytics for Medicine and Healthcare
language:
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Author:

Kathiravelu Pradeeban,Chen Yiru,Sharma Ashish,Galhardas Helena,Van Roy Peter,Veiga Luís

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-67186-4_2

Reference29 articles.

1. Lee, G., Doyle, S., Monaco, J., Madabhushi, A., Feldman, M.D., Master, S.R., Tomaszewski, J.E.: A knowledge representation framework for integration, classification of multi-scale imaging and non-imaging data: preliminary results in predicting prostate cancer recurrence by fusing mass spectrometry and histology. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 77–80. IEEE (2009)

2. Huang, Z.: Data Integration For Urban Transport Planning. Citeseer (2003)

3. Sujansky, W.: Heterogeneous database integration in biomedicine. J. Biomed. Inform. 34(4), 285–298 (2001)

4. Mildenberger, P., Eichelberg, M., Martin, E.: Introduction to the dicom standard. Eur. Radiol. 12(4), 920–927 (2002)

5. Whitcher, B., Schmid, V.J., Thornton, A.: Working with the DICOM and NIFTI data standards in R. J. Stat. Softw. 44(6), 1–28 (2011)

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1. On-demand big data integration;Distributed and Parallel Databases;2018-09-01

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