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What's PMML and what's new in PMML 4.0?

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Rick PechterAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 11, Issue 1

Pages 19 - 25

https://doi.org/10.1145/1656274.1656279

Published: 16 November 2009 Publication History

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Abstract

The Predictive Model Markup Language (PMML) data mining standard has arguably become one of the most widely adopted data mining standards in use today. Two years in the making, the latest release of PMML contains several new features and many enhancements to existing ones. This paper provides a primer on the PMML standard and its applications along with a description of the new features in PMML 4.0 which was released in May 2009.

References

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JSR 73: Data Mining API. See http://jcp.org/en/jsr/detail?id=73

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OLE DB for Data Mining. See http://msdn.microsoft.com/en-us/library/ms146608.aspx

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Data Mining Group Website. http://www.dmg.org

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[4]

R. Pechter. Conformance Standard for the Predictive Model Markup Language. KDD-2006 Workshop on Data Mining Standards, Services and Platforms. August, 2006.

Digital Library

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[5]

KDNuggets 2009 n9, item 1, Poll: Data mining deployment grows, especially PMML and Cloud options. May 12, 2009. See: http://www.kdnuggets.com/news/2009/n09-/1i.html

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Dai HWu JWang YYen JZhang YXu C(2023)Cost-Efficient Sharing Algorithms for DNN Model Serving in Mobile Edge NetworksIEEE Transactions on Services Computing10.1109/TSC.2023.324704916:4(2517-2531)Online publication date: 1-Jul-2023
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Ananda Padmanabhan ABalczewski ESingh K(2022)Artificial Intelligence Systems in CKD: Where Do We Stand and What Will the Future Bring?Advances in Chronic Kidney Disease10.1053/j.ackd.2022.06.00429:5(461-464)Online publication date: Sep-2022
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Sarnovsky MBednar PSmatana M(2019)Cross-Sectorial Semantic Model for Support of Data Analytics in Process IndustriesProcesses10.3390/pr70502817:5(281)Online publication date: 13-May-2019
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Index Terms

What's PMML and what's new in PMML 4.0?
1. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

ACM SIGKDD Explorations Newsletter Volume 11, Issue 1

June 2009

56 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1656274

Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2009

Published in SIGKDD Volume 11, Issue 1

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Dai HWu JWang YYen JZhang YXu C(2023)Cost-Efficient Sharing Algorithms for DNN Model Serving in Mobile Edge NetworksIEEE Transactions on Services Computing10.1109/TSC.2023.324704916:4(2517-2531)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TSC.2023.3247049
Ananda Padmanabhan ABalczewski ESingh K(2022)Artificial Intelligence Systems in CKD: Where Do We Stand and What Will the Future Bring?Advances in Chronic Kidney Disease10.1053/j.ackd.2022.06.00429:5(461-464)Online publication date: Sep-2022
https://doi.org/10.1053/j.ackd.2022.06.004
Sarnovsky MBednar PSmatana M(2019)Cross-Sectorial Semantic Model for Support of Data Analytics in Process IndustriesProcesses10.3390/pr70502817:5(281)Online publication date: 13-May-2019
https://doi.org/10.3390/pr7050281
Bartschat AReischl MMikut R(2019)Data mining toolsWIREs Data Mining and Knowledge Discovery10.1002/widm.13099:4Online publication date: 22-Feb-2019
https://doi.org/10.1002/widm.1309
Sarnovsky MBednar PSmatana M(2018)Big Data Processing and Analytics Platform Architecture for Process Industry FactoriesBig Data and Cognitive Computing10.3390/bdcc20100032:1(3)Online publication date: 26-Jan-2018
https://doi.org/10.3390/bdcc2010003
Nannapaneni SNarayanan AAk RLechevalier DSexton TMahadevan SLee Y(2018)Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in ManufacturingSmart and Sustainable Manufacturing Systems10.1520/SSMS201800182:1(20180018)Online publication date: 2-Oct-2018
https://doi.org/10.1520/SSMS20180018
Catania BMaddalena A(2018)A unified framework for heterogeneous patternsInformation Systems10.1016/j.is.2011.12.00137:5(460-483)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.is.2011.12.001
Sarnovsky MBednar PSmatana M(2017)Data integration in scalable data analytics platform for process industries2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)10.1109/INES.2017.8118553(000187-000192)Online publication date: Oct-2017
https://doi.org/10.1109/INES.2017.8118553
Supakkul SZhao LChung L(2016)GOMA: Supporting Big Data Analytics with a Goal-Oriented Approach2016 IEEE International Congress on Big Data (BigData Congress)10.1109/BigDataCongress.2016.26(149-156)Online publication date: Jun-2016
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Wang PJiang J(2016)Research on Data Mining and Electronic Commerce Information Push Application Based on PMML2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD)10.1109/ACIT-CSII-BCD.2016.089(426-430)Online publication date: Dec-2016
https://doi.org/10.1109/ACIT-CSII-BCD.2016.089
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