Scalable learning for dispersed knowledge systems

Charles Z. Liu; Manolya Kavakli

doi:10.1145/2994374.2994395

Scalable learning for dispersed knowledge systems

Charles Z. Liu^*, Manolya Kavakli

^*Corresponding author for this work

Macquarie University

Research output: Chapter in Book/Published conference output › Conference publication

4 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 language	English
Title of host publication	Proccedings of the 10th European Conference on Software Architecture Workshops, ECSAW 2016
Publisher	ACM
Pages	125-134
Number of pages	10
ISBN (Electronic)	9781450347501, 9781450347815
DOIs	https://doi.org/10.1145/2994374.2994395
Publication status	Published - 28 Nov 2016
Event	13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016 - Hiroshima, Japan Duration: 28 Nov 2016 → 1 Dec 2016

Publication series

Name	ACM International Conference Proceeding Series
Volume	28-November-2016

Conference

Conference	13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016
Country/Territory	Japan
City	Hiroshima
Period	28/11/16 → 1/12/16

Bibliographical note

Publisher Copyright:
© 2016 ACM.

Keywords

Dispersed knowledge system
Intelligent ubiquitous computing
Scalable learning
ξ process

Access to Document

10.1145/2994374.2994395

Cite this

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title = "Scalable learning for dispersed knowledge systems",

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.",

keywords = "Dispersed knowledge system, Intelligent ubiquitous computing, Scalable learning, ξ process",

author = "Liu, {Charles Z.} and Manolya Kavakli",

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Liu, CZ & Kavakli, M 2016, Scalable learning for dispersed knowledge systems. in Proccedings of the 10th European Conference on Software Architecture Workshops, ECSAW 2016. ACM International Conference Proceeding Series, vol. 28-November-2016, ACM, pp. 125-134, 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016, Hiroshima, Japan, 28/11/16. https://doi.org/10.1145/2994374.2994395

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AU - Kavakli, Manolya

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AB - 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.

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KW - Scalable learning

KW - ξ process

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M3 - Conference publication

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Scalable learning for dispersed knowledge systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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