Scalable learning for dispersed knowledge systems

Charles Z. Liu*, Manolya Kavakli

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

Abstract

This paper mainly focuses on dealing with the issue of scalable learning in dispersed knowledge system. A scalable learning scheme and ξ process are proposed with a theoretical analysis. With the proposed scheme, the dispersed knowledge system can be used as a centralized system without knowing the overview of the global database. A case study of application in dispersed face recognition system is given to show how the proposed scheme implements and works.

Original languageEnglish
Title of host publicationProccedings of the 10th European Conference on Software Architecture Workshops, ECSAW 2016
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages125-134
Number of pages10
Volume28-November-2016
ISBN (Electronic)9781450347501, 9781450347815
DOIs
Publication statusPublished - 28 Nov 2016
Event13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016 - Hiroshima, Japan
Duration: 28 Nov 20161 Dec 2016

Other

Other13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016
CountryJapan
CityHiroshima
Period28/11/161/12/16

Keywords

  • Dispersed knowledge system
  • Intelligent ubiquitous computing
  • Scalable learning
  • ξ process

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