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http://dx.doi.org/10.7472/jksii.2018.19.1.123

A Data-driven Approach for Computational Simulation: Trend, Requirement and Technology  

Lee, Sunghee (KISTI)
Ahn, Sunil (KISTI)
Joo, Wonkyun (KISTI)
Yang, Myungseok (KISTI)
Yu, Eunji (KISTI)
Publication Information
Journal of Internet Computing and Services / v.19, no.1, 2018 , pp. 123-130 More about this Journal
Abstract
With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.
Keywords
data-driven; computational science; trend; platform;
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1 T. Ogata and Y. Masayoshi, "New stage of MatNavi, materials database at NIMS," 2012, http://mits.nims.go.jp/index_en.html
2 NoMaD Repository, http://nomad-repository.eu.
3 W. Kamp, and et al. "Dynameomics: a comprehensive database of protein dynamics," Structure, Vol 18. No. 4, 2010. http://dx.doi.org/10.1016/j.str.2010.01.012   DOI
4 T. Meyer, and et al. "MoDEL (Molecular Dynamics Extended Library): a database of atomistic molecular dynamics trajectories," Structure, Vol 18, No. 11, pp. 1399-1409, 2010. http://dx.doi.org/10.1016/j.str.2010.07.013   DOI
5 J. Westbrook, and et al. "The protein data bank: unifying the archive." Nucleic acids research, Vol. 30, No. 1, 2002, pp. 245-248. https://doi.org/10.1093/nar/30.1.245   DOI
6 P. Andrio, and et al. "BIGNASim: a NoSQL database structure and analysis portal for nucleic acids simulation data," Nucleic acids research, Vol 44,2016, pp. 272-278. http://dx.doi.org/10.1093/nar/gkv1301   DOI
7 C. Thibault, F. Julien, and C. Thomas, "IBIOMES: managing and sharing biomolecular simulation data in a distributed environment," Journal of chemical information and modeling, Vol 53. No. 3, 2014, pp. 726-736. http://dx.doi.org/10.1021/ci300524j   DOI
8 V. Chekanov, "HepSim: a repository with predictions for high-energy physics experiments," Advances in High Energy Physics 2015, 2015. http://dx.doi.org/10.1155/2015/136093   DOI
9 G. Klimeck, and et. al. "nanohub. org: Advancing education and research in nanotechnology," Computing in Science & Engineering, Vol. 10, No. 5, 2008, pp. 17-23. http://dx.doi.org/10.1109/MCSE.2008.120   DOI
10 M. McLennan, and K. Rick, "HUBzero: a platform for dissemination and collaboration in computational science and engineering," Computing in Science & Engineering, Vol. 12, No. 2, 2010. http://dx.doi.org/10.1109/MCSE.2010.41   DOI
11 T. J. Hacker, and et. al. "The NEEShub cyberinfrastructure for earthquake engineering," Computing in Science & Engineering, Vol. 13, No. 4, 2011, pp. 67-78. http://dx.doi.org/10.1109/MCSE.2011.70   DOI
12 G. Pizzi, and et. al., "AiiDA: Automated interactive infrastructure and database for computational science," Computational Materials Science, Vol 111, 2016. https://doi.org/10.1016/j.commatsci.2015.09.013   DOI
13 R. Tansley, and et. al. "The DSpace institutional digital repository system: current functionality," Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries. IEEE Computer Society, 2003. http://dx.doi.org/10.1002/asi.10018
14 D. Wilcox, and W. Evviv, "Supporting Digital Preservation and Access with Fedora," IFLA WLIC 2017, 2017. http://dx.doi.org/10.1007/3-540-49653-x_4   DOI
15 K. Stapelfeldt and M. Donald, "Islandora and TEI: Current and Emerging Applications/ Approaches," Journal of the Text Encoding Initiative, Vol 5, 2013. http://dx.doi.org/10.4000/jtei.790   DOI
16 A. Kumar, and et. al. "DCMS: A data analytics and management system for molecular simulation," Journal of big data, Vol. 2, No. 1, 2014. https://doi.org/10.1186/s40537-014-0009-5   DOI
17 W. K. Michener, and B. J. Matthew, "Ecoinformatics: supporting ecology as a data-intensive science," Trends in ecology & evolution, Vol. 27, No. 2, 2012, pp. 85-93. http://dx.doi.org/10.1016/j.tree.2011.11.016   DOI
18 G. King, "An Introduction to the Dataverse Network as an Infrastructure for Data Sharing," Sociological Methods and Research, Vol. 36, 2007, pp. 173-199. http://dx.doi.org/10.1177/0049124107306660   DOI
19 C. Lagoze, and et. al. "The Open Archives Initiative Protocol for Metadata Harvesting," http://www.openarchives.org/OAI/2.0/openarchivesprotocol.htm, 2015, https://doi.org/10.1108/07378830310479776   DOI
20 J. Allinson, F. Sebastien, and L. Stuart, "SWORD: Simple Web-service offering repository deposit." Ariadne, Vol. 54, 2008. https://doi.org/10.1045/january2012-lewis   DOI
21 W. Michener, and et al. "DataONE: Data Observation Network for Earth-Preserving data and enabling innovation in the biological and environmental sciences," D-Lib Magazine, Vol. 17, No. 1/2, 2011. http://dx.doi.org/10.1045/january2011-michener   DOI
22 P. Giannozzi, et al. "QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials," Journal of physics: Condensed matter, Vol. 21, No. 39, 2009. http://dx.doi.org/10.1007/3-540-35426-3-17
23 D. Dietrich, and P. Rufus, "CKAN: apt-get for the debian of data," 26th chaos communication congress, 2009. https://ckan.org/
24 A. B. Nosek, et al. "Promoting an open research culture," Science, Vol. 348, No. 6242, pp. 1422-1425., 2015. http://dx.doi.org/10.1126/science.aab2374   DOI
25 Lee, Ki Yong, et al. "Design and implementation of a data-driven simulation service system," Proceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory. ACM, 2016. http://dx.doi.org/10.1145/3007818.3007826   DOI
26 S. R. Jeong, and G. Imran, "Semantic Computing for Big Data: Approaches, Tools and Emerging Directions (2011-2014)," KSII Transactions on Internet & Information Systems, Vol. 8, No. 6, 2014. http://dx.doi.org/10.3837/tiis.2014.06.012   DOI
27 K. Y. Kim, "Business Intelligence and Marketing Insights in an Era of Big Data: The Q-sorting Approach," KSII Transactions on Internet & Information Systems, Vol. 8, No. 2, 2014. http://dx.doi.org/10.3837/tiis.2014.02.014   DOI
28 M. Chung, J. Kim. "The Internet Information and Technology Research Directions based on the Fourth Industrial Revolution," KSII Transactions on Internet & Information Systems, Vol. 10, No.3, 2016. http://dx.doi.org/10.3837/tiis.2016.03.020   DOI
29 W. Joo, and et. al. "A Trend of Data-driven Approach for Computer Simulation," ICONI2016, 2016
30 J. Hafner, "Ab-initio simulations of materials using VASP: Density-functional theory and beyond," Journal of computational chemistry, Vol. 29, No. 13, 2008, pp. 2044-2078. http://dx.doi.org/10.1002/jcc.21057   DOI
31 A. Jain, and et al., "Commentary: The Materials Project: A materials genome approach to accelerating materials innovation," Apl Materials, Vol. 1, No. 1, 2013. https://doi.org/10.1063/1.4812323   DOI
32 G. Blaha, and et. al. "WIEN2k, An Augmented Plane Wave+ Local Orbitals Program for Calculating Crystal Properties," 2001. http://www.citeulike.org/user/rcollyer/article/6205108
33 J. Anubhav, P. Kristin, C. Gerbrand, "Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases," APL Materials, Vol. 4. No. 5, 2016. http://dx.doi.org/10.1063/1.4944683   DOI