EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 12 (2017) ITM Web Conf., 12 (2017) 01014 References
Open Access
Issue
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
Article Number 01014
Number of page(s) 6
Section Session 1: Robotics
DOI https://doi.org/10.1051/itmconf/20171201014
Published online 05 September 2017
  1. W. Le, F. Li, A. Kementsietsidis, and S. Duan, Scalable Keyword Search on Large RDF Data, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 11, pp. 2774–2788, 2014. [CrossRef] [Google Scholar]
  2. H. He, H. Wang, J. Yang, and P. S. Yu, BLINKS: Ranked Keyword Searches on Graphs, ACM SIGMOD International Conference on Management of Data, pp. 305–316, 2007. [Google Scholar]
  3. D. Florescu, D. Kossmann, and I. Manolescu, Integrating Keyword Search into XML Query Processing, international world wide web conferences, vol. 33, no. 1, pp. 119–135, 2000. [Google Scholar]
  4. S. Cohen, J. Mamou, Y. Kanza, and Y. Sagiv, XSEarch: A Semantic Search Engine for XML, Very Large Data Bases, pp. 45–56, 2003. [Google Scholar]
  5. L. Guo, F. Shao, C. Botev, and J. Shanmugasundaram, XRANK: Ranked Keyword Search over XML Documents, ACM SIGMOD International Conference on Management of Data ACM, pp. 16–27, 2003. [Google Scholar]
  6. R. Kaushik, R. Krishnamurthy, J. F. Naughton, and R. Ramakrishnan, On the Integration of Structure Indexes and Inverted Lists, pp. 779–790, 2004. [Google Scholar]
  7. Y. Li, C. Yu, and H. V. Jagadish, Schema-Free XQuery, Very Large Data Bases, pp. 72–83, 2004. [Google Scholar]
  8. S. Agrawal, S. Chaudhuri, and G. Das, DBXplorer: A System for Keyword-Based Search over Relational Databases, International Conference on Data Engineering, 2002. [Google Scholar]
  9. G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan, Keyword Searching and Browsing in Databases using BANKS, pp. 431–440, 2002. [Google Scholar]
  10. V. Hristidis, and Y. Papakonstantinou, DISCOVER: Keyword Search in Relational Databases, Very Large Data Bases, pp. 670–681, 2002. [Google Scholar]
  11. V. Hristidis, L. Gravano, and Y. Papakonstantinou, Efficient IR-Style Keyword Search over Relational Databases, Very Large Data Bases, pp. 850–861, 2003. [Google Scholar]
  12. F. Liu, C. Yu, W. Meng, and A. Chowdhury, Effective Keyword Search in Relational Databases, pp. 563–574, 2006. [Google Scholar]
  13. V. Kacholia, S. Pandit, S. Chakrabarti, S. Sudarshan, R. Desai, and H. Karambelkar, Bidirectional Expansion For Keyword Search on Graph Databases, Very Large Data Bases, pp. 505–516, 2005. [Google Scholar]
  14. B. Ding, J. X. Yu, S. Wang, L. Qin, X. Zhang, and X. Lin, Finding Top-k Min-Cost Connected Trees in Databases, pp. 836–845, 2007. [EDP Sciences] [Google Scholar]
  15. B. Dalvi, M. Kshirsagar, and S. Sudarshan, Keyword Search on External Memory Data Graphs, Proceedings of The Vldb Endowment, vol. 1, no. 1, pp. 1189–1204, 2008. [CrossRef] [Google Scholar]
  16. J. Shi, D. Wu, and N. Mamoulis, Top-k Relevant Semantic Place Retrieval on Spatial RDF Data, pp. 1977–1990, 2016. [Google Scholar]
  17. S. Elbassuoni and R. Blanco, Keyword Search over RDF Graphs, pp. 237–242, 2011. [Google Scholar]
  18. T. Tran, H. Wang, S. Rudolph, and P. Cimiano, Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data, pp. 405–416, 2009. [Google Scholar]
  19. X. Lian, E. De Hoyos, A. Chebotko, B. Fu, and C. F. Reilly, k-nearest keyword search in RDF graphs, Journal of Web Semantics, vol. 22, no. 0, pp. 40–56, 2013. [CrossRef] [Google Scholar]
  20. C. Halaschek, B. Alemanmeza, I. B. Arpinar, and A. P. Sheth, Discovering and Ranking Semantic Associations over a Large RDF metabase, Very Large Data Bases, pp. 1317–1320, 2004. [Google Scholar]
  21. H. Fu and K. Anyanwu, Effectively Interpreting Keyword Queries on RDF Databases with a Rear View, pp. 193–208, 2011. [Google Scholar]
  22. H. Wang, and C. C. Aggarwal, A Survey of Algorithms for Keyword Search on Graph Data, Managing and Mining Graph Data. Springer US, pp. 249–273, 2010. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.