tutorial

Parallel algorithms for mining large-scale rich-media data

Authors:

Edward Y. Chang,

Hongjie Bai,

Kaihua ZhuAuthors Info & Claims

MM '09: Proceedings of the 17th ACM international conference on Multimedia

Pages 917 - 918

https://doi.org/10.1145/1631272.1631451

Published: 19 October 2009 Publication History

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Abstract

The amount of online photos and videos is now at the scale of tens of billions. To organize, index, and retrieve these large-scale rich-media data, a system must employ scalable data management and mining algorithms. The research community needs to consider solving large scale problems rather than solving problems with small datasets that do not reflect real life scenarios. This tutorial introduces key challenges in large-scale rich-media data mining, and presents parallel algorithms for tackling such challenges. We present our parallel implementations of Spectral Clustering (PSC), FP-Growth (PFP), Latent Dirichlet Allocation (PLDA), and Support Vector Machines (PSVM).

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PLDA Open Source http://code.google.com/p/plda/.

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Cited By

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Razzaq AAhmad AMalik AFahmideh MRamadan R(2023)Software engineering for internet of underwater things to analyze oceanic dataInternet of Things10.1016/j.iot.2023.10089324(100893)Online publication date: Dec-2023
https://doi.org/10.1016/j.iot.2023.100893
Sood ISharma V(2020)A comparative study of Distributed Large Scale Data Mining AlgorithmsBSSS Journal of Computer10.51767/jc1102Online publication date: 25-May-2020
https://doi.org/10.51767/jc1102
Pramanik MLau RAzad MHossain MChowdhury MKarmaker B(2020)Healthcare informatics and analytics in big dataExpert Systems with Applications10.1016/j.eswa.2020.113388152(113388)Online publication date: Aug-2020
https://doi.org/10.1016/j.eswa.2020.113388
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Index Terms

Parallel algorithms for mining large-scale rich-media data
1. Computing methodologies
  1. Machine learning

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Information & Contributors

Information

Published In

MM '09: Proceedings of the 17th ACM international conference on Multimedia

October 2009

1202 pages

ISBN:9781605586083

DOI:10.1145/1631272

General Chairs:
Wen Gao
Peking University, China
,
Yong Rui
Microsoft, China
,
Alan Hanjalic
Delft University of Technology, The Netherlands
,
Program Chairs:
Changsheng Xu
Institute of Automation, Chinese Academy of Sciences, China
,
Eckehard Steinbach
Technical University of Munich, Germany
,
Abdulmotaleb El Saddik
University of Ottawa, Canada
,
Michelle Zhou
IBM T. J. Watson Research Center, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2009

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Tutorial

Conference

MM09

Sponsor:

SIGMM

MM09: ACM Multimedia Conference

October 19 - 24, 2009

Beijing, China

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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Razzaq AAhmad AMalik AFahmideh MRamadan R(2023)Software engineering for internet of underwater things to analyze oceanic dataInternet of Things10.1016/j.iot.2023.10089324(100893)Online publication date: Dec-2023
https://doi.org/10.1016/j.iot.2023.100893
Sood ISharma V(2020)A comparative study of Distributed Large Scale Data Mining AlgorithmsBSSS Journal of Computer10.51767/jc1102Online publication date: 25-May-2020
https://doi.org/10.51767/jc1102
Pramanik MLau RAzad MHossain MChowdhury MKarmaker B(2020)Healthcare informatics and analytics in big dataExpert Systems with Applications10.1016/j.eswa.2020.113388152(113388)Online publication date: Aug-2020
https://doi.org/10.1016/j.eswa.2020.113388
Barua HMondal K(2019)A Comprehensive Survey on Cloud Data Mining (CDM) Frameworks and AlgorithmsACM Computing Surveys10.1145/334926552:5(1-62)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3349265
Tavara S(2019)Parallel Computing of Support Vector MachinesACM Computing Surveys10.1145/328098951:6(1-38)Online publication date: 28-Jan-2019
https://dl.acm.org/doi/10.1145/3280989
Tayal VSrivastava R(2018)Challenges in Mining Big Data StreamsData and Communication Networks10.1007/978-981-13-2254-9_15(173-183)Online publication date: 30-Dec-2018
https://doi.org/10.1007/978-981-13-2254-9_15
Liu YQiu MLiu CGuo Z(2017)Big data challenges in ocean observationPersonal and Ubiquitous Computing10.1007/s00779-016-0980-221:1(55-65)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s00779-016-0980-2
Radhika DAruna Kumari D(2017)Adding Big Value to Big Businesses: A Present State of the Art of Big Data, Frameworks and AlgorithmsICT Based Innovations10.1007/978-981-10-6602-3_17(171-184)Online publication date: 1-Oct-2017
https://doi.org/10.1007/978-981-10-6602-3_17
Amma N(2016)Big Data MiningEffective Big Data Management and Opportunities for Implementation10.4018/978-1-5225-0182-4.ch003(53-59)Online publication date: 2016
https://doi.org/10.4018/978-1-5225-0182-4.ch003
Ji SZhang DZhang L(2016)Paths sharing based FP-growth data mining algorithms2016 8th International Conference on Wireless Communications & Signal Processing (WCSP)10.1109/WCSP.2016.7752497(1-4)Online publication date: Oct-2016
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