Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach-Reference-Cited by-同舟云学术

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Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

Published:2017-10-26 Issue:1 Volume:4 Page:
ISSN:2196-1115
Container-title:Journal of Big Data
language:en
Short-container-title:J Big Data

Author:

Mukherjee Sanjoy Kumar,Bandyopadhyay Sivaji^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Information Systems and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems

Link

http://link.springer.com/content/pdf/10.1186/s40537-017-0097-0.pdf

Reference156 articles.

1. Smart. Knowledge-intensive fusion for situational awareness: requirement for knowledge filtered awareness. Technical Report, University of Southampton, Web and Internet Science ePrintId 261067; 2005.

2. Malik, Gehani, Tarique, et al. Sketching distributed data provenance. Book Chapter, Data Provenance and Data Management in e-science. Berlin: Springer; 2013.

3. Deutsch, Milo, Roy, Tannen. Circuits for DataLog Provenance. In: ICDT; 2014. p. 201–12.

4. Bates, Mood, Valafar, et al. Towards secure provenance based access control in cloud environments. In: Proceedings of CODASPY; 2013. p. 277–84.

5. Green, Karvounarakis, Ives, Tannen. Update exchange with mappings and provenance. In: Proceedings of VLDB; 2007. p. 675–86.

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