Hadoop and PaaS Collaborative Practice Research in Video Monitoring Platform

Wei Dong Zhang; Xiang Qian Ding; Rui Chun Hou

doi:10.4028/www.scientific.net/AMM.543-547.3549

Paper Titles

The Designation of Intrusion Detection System Model Based on Data Mining
p.3532

A Process Model for Software Testing
p.3537

An Analysis of Cooperative Network Coding in Two-Way Relay System
p.3541

An Enhanced Monitoring Mechanism for IaaS Platforms
p.3545

Hadoop and PaaS Collaborative Practice Research in Video Monitoring Platform
p.3549

A Novel Routing Scheme for ICN
p.3555

Research on Storage Algorithm of Video Stream in Video Surveillance System Based on H.264
p.3561

Evaluation and Analysis of the Quantitative Computing Model of the Information Security Risk Assessment
p.3565

Discovery Algorithm of the Association Rules Based on Cloud Database
p.3569

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547Hadoop and PaaS Collaborative Practice Research in...

Hadoop and PaaS Collaborative Practice Research in Video Monitoring Platform

Abstract:

Cloud computing is an important area of current information technology research, particularly into the era of big data, Platform as a Service (PaaS) has become the industry to explore one of the hot clouds computing. But the function of the composition and architecture of platform services not yet conclusive, the paper on the application of Video monitoring platform through collaborative practice hadoop and PaaS, and proposed a new cloud computing platform Video monitoring solutions, PaaS and various modules under this program function overview. Provides a reference model for the era of big data Hadoop and PaaS collaborative applications.

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Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

3549-3554

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.3549

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Online since:

March 2014

Authors:

Wei Dong Zhang*, Xiang Qian Ding, Rui Chun Hou

Keywords:

Big Data Storage, Distributed Computing, Hadoop, PaaS

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* - Corresponding Author

References

[1] Shvachko K., Hairong Kuang, Radia S., Chansler R.: The Hadoop Distributed File System/ Proc. of 26th IEEE Symposium on Mass Storage Systems and Technologies, Piscataway, 2010, NJ: IEEE Press, 1-10.

DOI: 10.1109/msst.2010.5496972

Google Scholar

[2] Dean, J. and S. Ghemawat, MapReduce: Simplified data processing on large clusters, USENIX Association Proceedings of the Sixth Symposium on Operating Systems Design and Implementation(OSDE '04), 2004: pp.137-149.

Google Scholar

[3] Celina M, Olszak and Ewa Ziemba, in: Approach toBuilding and Implementing Business Intelligence, Interdisciplinary Journal of Information, Knowledge, and Management. (2007).

Google Scholar

[4] Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters, Communications of the ACM, 2008, 51(1): 107-113.

DOI: 10.1145/1327452.1327492

Google Scholar

[5] Dean, J. and S. Ghemawat, MapReduce: Simplified data processing on large clusters, USENIX Association Proceedings of the Sixth Symposium on Operating Systems Design and Implementation(OSDE '04), 2004: pp.137-149.

Google Scholar

[6] Y. L Fang and Han Bing: Design and Implementation of ETL Management Tool, Knowledge Acquisition and Modeling, (2009).

Google Scholar

[7] Zaki M J. Fast Vertical Mining Using Diffsets, New York, USA: Rensselaer Polytechnic Institute, Technical Report: 01-1, (2001).

Google Scholar

[8] W. H Fan, T. Y Xiao: Integrated architecture of cloud manufacturing based on federation mode, Computer Integrated manufacturing Systems, 2011, 17(3): 469-476(in chinese).

Google Scholar

[9] Information on http: /en. wikipedia. org/wiki/Google_earth [EB/OL].

Google Scholar