Abstract
Data-driven analysis is critically important for creating new insights and values; however, data silos prevent a full utilization of existing data. To overcome this issue, we propose Semantic Data MAP (SD-MAP). SD-MAP utilizes a virtual graph and conceptual groups to bypass a physical integration of databases while allowing users to access data using intuitive queries. In order to show our system’s effectiveness, we introduce a plausible scenario with a data structure from real world. A demonstration video of SD-MAP is available at: https://youtu.be/i7U_8763Ogk.
S. Yi, S.-H. Kim and J. Park—These authors contributed equally.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Tiwari, S., Wee, H., Daryanto, Y.: Big data analytics in supply chain management between 2010 and 2016: insights to industries. Comput. Ind. Eng. 115, 319–330 (2018)
Dong, X.L., Halevy, A., Yu, C.: Data integration with uncertainty. VLDB J. 18(2), 469–500 (2009)
Calvanese, D., et al.: Ontop: answering SPARQL queries over relational databases. Semant. Web 8(3), 471–487 (2017)
Renée, M., Laura, H., Mauricio, H.: Schema mapping as query discovery. In: Proceedings of the 26th International Conference on Very Large Data Bases, VLDB 2000, pp. 77–88 (2000)
Shen, W., Wang, J., Han, J.: Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans. Knowl. Data Eng. 27(2), 443–460 (2015)
vis.js Homepage. http://visjs.org/
Teiid Homepage. http://teiid.io/
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yi, S., Kim, SH., Park, J., Hwang, T., Lee, J., Lee, Y. (2019). I Know Where to Find What You Want: Semantic Data Map from Silos. In: Hitzler, P., et al. The Semantic Web: ESWC 2019 Satellite Events. ESWC 2019. Lecture Notes in Computer Science(), vol 11762. Springer, Cham. https://doi.org/10.1007/978-3-030-32327-1_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-32327-1_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32326-4
Online ISBN: 978-3-030-32327-1
eBook Packages: Computer ScienceComputer Science (R0)