DocumentCode :
48612
Title :
An Overlay-Based Data Mining Architecture Tolerant to Physical Network Disruptions
Author :
Suto, Kuniaki ; Nishiyama, Hiroki ; Kato, Nei ; Mizutani, Keiichi ; Akashi, Osamu ; Takahara, Atsushi
Author_Institution :
Grad. Sch. of Inf. Sci., Tohoku Univ., Sendai, Japan
Volume :
2
Issue :
3
fYear :
2014
fDate :
Sept. 2014
Firstpage :
292
Lastpage :
301
Abstract :
Management scheme for highly scalable big data mining has not been well studied in spite of the fact that big data mining provides many valuable and important information for us. An overlay-based parallel data mining architecture, which executes fully distributed data management and processing by employing the overlay network, can achieve high scalability. However, the overlay-based parallel mining architecture is not capable of providing data mining services in case of the physical network disruption that is caused by router/communication line breakdowns because numerous nodes are removed from the overlay network. To cope with this issue, this paper proposes an overlay network construction scheme based on node location in physical network, and a distributed task allocation scheme using overlay network technology. The numerical analysis indicates that the proposed schemes considerably outperform the conventional schemes in terms of service availability against physical network disruption.
Keywords :
Big Data; data mining; numerical analysis; parallel processing; resource allocation; big data mining; distributed data management; distributed task allocation scheme; node location; numerical analysis; overlay network construction scheme; overlay-based parallel data mining architecture; physical network disruptions; Computer architecture; Data mining; Electric breakdown; Overlay networks; Scalability; Servers; Big data mining; neighbor selection; overlay network; physical network disruption; service availability; task allocation;
fLanguage :
English
Journal_Title :
Emerging Topics in Computing, IEEE Transactions on
Publisher :
ieee
ISSN :
2168-6750
Type :
jour
DOI :
10.1109/TETC.2014.2330517
Filename :
6832507
Link To Document :
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