tutorial

Machine learning for big data

Authors:

Neoklis Polyzotis,

Markus WeimerAuthors Info & Claims

SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

Pages 939 - 942

https://doi.org/10.1145/2463676.2465338

Published: 22 June 2013 Publication History

Abstract

Statistical Machine Learning has undergone a phase transition from a pure academic endeavor to being one of the main drivers of modern commerce and science. Even more so, recent results such as those on tera-scale learning [1] and on very large neural networks [2] suggest that scale is an important ingredient in quality modeling. This tutorial introduces current applications, techniques and systems with the aim of cross-fertilizing research between the database and machine learning communities.

The tutorial covers current large scale applications of Machine Learning, their computational model and the workflow behind building those. Based on this foundation, we present the current state-of-the-art in systems support in the bulk of the tutorial. We also identify critical gaps in the state-of-the-art. This leads to the closing of the seminar, where we introduce two sets of open research questions: Better systems support for the already established use cases of Machine Learning and support for recent advances in Machine Learning research.

References

[1]

A. Agarwal, O. Chapelle, M. Dudik and J. Langford, "A Reliable Effective Terascale Linear Learning System," arXiv.org, 2012.

[2]

J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. Le, M. Mao, A. Senior, P. Tucker, K. Yang and A. Ng, "Large Scale Distributed Deep Networks," in Advances in Neural Information Processing Systems, 2013.

[3]

The Apache Project, "Apache Hadoop NextGen MapReduce (YARN)," The Apache Project, {Online}. Available: http://hadoop.apache.org/docs/r0.23.0/hadoop-yarn/hadoop-yarn-site/YARN.html.

[4]

B. Hindman, A. Konwinski, M. Zaharia and I. Stoica, "A Common Substrate for Cluster Computing," in HotCloud, 2009.

Digital Library

[5]

M. Kearns, "Efficient noise-tolerant learning from statistical queries," Journal of the ACM, pp. 392--401, 1998.

Digital Library

[6]

C.-T. Chu, S. K. Kim, Y.-A. Lin, Y. Yu, G. Bradski and A. Y. Ng, "Map-Reduce for Machine Learning on Multicore," in Advances in Neural Information Processing Systems 19, Cambridge, MA, 2007.

[7]

The Apache Foundation, "Apache Pig," 11 12 2012. {Online}. Available: http://pig.apache.org/.

[8]

J. Dean and S. Ghemat, "MapReduce: simplified data processing on large clusters," Communications of the ACM, vol. 51, pp. 107--113, 2008.

Digital Library

[9]

The Apache Mahout Project, "Apache Mahout," 17 9 2012. {Online}. Available: http://mahout.apache.org/. {Accessed 17 9 2012}.

[10]

M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. J. Franklin, S. Shenker and I. Stoica, "Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing," in USENIX NSDI, San Jose, CA, 2012.

Digital Library

[11]

G. Malewicz, M. H. Austern, A. J. Bik, J. C. Dehnert, I. Horn, N. Leiser and G. Czajkowski, "Pregel: a system for large-scale graph processing," in Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, Indianapolis, Indiana, USA, 2010.

Digital Library

[12]

The Apache Software Foundation, "Apache Giraph," {Online}. Available: http://giraph.apache.org/.

[13]

Y. Low, D. Bickson, J. Gonzalez, C. Guestrin, A. Kyrola and J. M. Hellerstein, "Distributed GraphLab: a framework for machine learning and data mining in the cloud," Proceedings of the VLDB Endowment, vol. 5, no. 8, pp. 716--727, April 2012.

Digital Library

[14]

G. Hinton, "Learning Multiple Layers of Representation," Trends in Cognitive Sciences, vol. 11, pp. 428--434, 2007.

[15]

G. E. Hinton, S. Osindero and Y. Teh, "A fast learning algorithm for deep belief nets," Neural Computation, vol. 18, pp. 1526--1554, 2006.

Digital Library

[16]

J. Markoff, "How Many Computers to Identify a Cat? 16,000," The New York Times, 25 June 2012. {Online}. Available: http://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html. {Accessed 11 December 2012}.

[17]

A. Smola, A. Ahmed and M. Weimer, "WWW 2012 Tutorial: New Templates for Scalable Data Analysis," June 2012. {Online}. Available: http://www2012.wwwconference.org/program/tutorials/ and http://cs.markusweimer.com/2012/04/06/www-2012-tutorial-new-templates-for-scalable-data-analysis/.

[18]

G. Dror, N. Koenigstein, Y. Koren and M. Weimer, "The Yahoo! Music Dataset and KDD-Cup'11," in Proceedings of KDDCup 2011, San Diego, CA, 2011.

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Index Terms

Machine learning for big data
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

June 2013

1322 pages

ISBN:9781450320375

DOI:10.1145/2463676

General Chairs:
Kenneth Ross
Columbia University
,
Divesh Srivastava
AT&T Research
,
Program Chair:
Dimitris Papadias
HKUST

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

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Published: 22 June 2013

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SIGMOD/PODS'13

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SIGMOD

SIGMOD/PODS'13: International Conference on Management of Data

June 22 - 27, 2013

New York, New York, USA

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SIGMOD '13 Paper Acceptance Rate 76 of 372 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Yao ZWang HYan WWang ZZhang WWang ZZhang G(2023)Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI imagesEuropean Journal of Radiology10.1016/j.ejrad.2023.110934165(110934)Online publication date: Aug-2023
https://doi.org/10.1016/j.ejrad.2023.110934
Ma QTriantafillou P(2022)Query-centric regressionInformation Systems10.1016/j.is.2021.101736104:COnline publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1016/j.is.2021.101736
Lee HChao LHsu C(2021)A 10-Year Probability Deep Neural Network Prediction Model for Lung CancerCancers10.3390/cancers1304092813:4(928)Online publication date: 23-Feb-2021
https://doi.org/10.3390/cancers13040928
Yin LZhang YZhang ZPeng YZhao P(2021)ParaXProceedings of the VLDB Endowment10.14778/3447689.344769214:6(864-877)Online publication date: 12-Apr-2021
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