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

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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
Place of Publication	New York
Publisher	Association for Computing Machinery
Pages	125-134
Number of pages	10
Volume	28-November-2016
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

Other

Other	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

Keywords

Dispersed knowledge system
Intelligent ubiquitous computing
Scalable learning
ξ process

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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",

year = "2016",

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doi = "10.1145/2994374.2994395",

language = "English",

volume = "28-November-2016",

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publisher = "Association for Computing Machinery",

note = "13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016 ; Conference date: 28-11-2016 Through 01-12-2016",

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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. vol. 28-November-2016, Association for Computing Machinery, New York, 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

Scalable learning for dispersed knowledge systems. / Liu, Charles Z.; Kavakli, Manolya.
Proccedings of the 10th European Conference on Software Architecture Workshops, ECSAW 2016. Vol. 28-November-2016 New York: Association for Computing Machinery, 2016. p. 125-134.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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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.

KW - Dispersed knowledge system

KW - Intelligent ubiquitous computing

KW - Scalable learning

KW - ξ process

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AN - SCOPUS:85007600046

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EP - 134

BT - Proccedings of the 10th European Conference on Software Architecture Workshops, ECSAW 2016

PB - Association for Computing Machinery

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T2 - 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2016

Y2 - 28 November 2016 through 1 December 2016

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Scalable learning for dispersed knowledge systems

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