PMML in manufacturing applications

Y. Tina Lee; Ju Yeon Lee

PMML in manufacturing applications

Y. Tina Lee
, Ju Yeon Lee

Dept. of Mechanical System and Design Engineering

National Institute of Standards and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In manufacturing, an enormous amount of data, both real and simulation data, is being continuously generated. The appropriate information, if extracted from big data, could provide insights on increasing sustainability, productivity, flexibility, and competitive advantages and eventually contribute to achieving the objectives of smart manufacturing on agility, asset utilization, and sustainability. The challenge is to reduce information overload for manufacturing and filter useful information to get the same detail level of manufacturing insights. The adoption of manufacturing data analytics in a timely manner can facilitate moving traditional manufacturing to agile, and eventually, smart manufacturing. This paper addresses how to apply a standardized predictive modeling technique onto manufacturing data analytics applications.

Original language	English
Title of host publication	Fall Simulation Interoperability Workshop, 2014 Fall SIW
Publisher	SISO - Simulation Interoperability Standards Organization
Pages	113-117
Number of pages	5
ISBN (Electronic)	9781634393898
State	Published - 2014
Event	Fall Simulation Interoperability Workshop, 2014 Fall SIW - Orlando, United States Duration: 8 Sep 2014 → 12 Sep 2014

Publication series

Name	Fall Simulation Interoperability Workshop, 2014 Fall SIW

Conference

Conference	Fall Simulation Interoperability Workshop, 2014 Fall SIW
Country/Territory	United States
City	Orlando
Period	8/09/14 → 12/09/14

Keywords

Data analytics
Manufacturing
PMML
Predictive model

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@inproceedings{07dc29da863048459e368d5b707d82af,

title = "PMML in manufacturing applications",

abstract = "In manufacturing, an enormous amount of data, both real and simulation data, is being continuously generated. The appropriate information, if extracted from big data, could provide insights on increasing sustainability, productivity, flexibility, and competitive advantages and eventually contribute to achieving the objectives of smart manufacturing on agility, asset utilization, and sustainability. The challenge is to reduce information overload for manufacturing and filter useful information to get the same detail level of manufacturing insights. The adoption of manufacturing data analytics in a timely manner can facilitate moving traditional manufacturing to agile, and eventually, smart manufacturing. This paper addresses how to apply a standardized predictive modeling technique onto manufacturing data analytics applications.",

keywords = "Data analytics, Manufacturing, PMML, Predictive model",

author = "Lee, \{Y. Tina\} and Lee, \{Ju Yeon\}",

note = "Publisher Copyright: Copyright {\textcopyright} (2014) by SISO - Simulation Interoperability Standards Organization All rights reserved.; Fall Simulation Interoperability Workshop, 2014 Fall SIW ; Conference date: 08-09-2014 Through 12-09-2014",

year = "2014",

language = "English",

series = "Fall Simulation Interoperability Workshop, 2014 Fall SIW",

publisher = "SISO - Simulation Interoperability Standards Organization",

pages = "113--117",

booktitle = "Fall Simulation Interoperability Workshop, 2014 Fall SIW",

}

TY - GEN

T1 - PMML in manufacturing applications

AU - Lee, Y. Tina

AU - Lee, Ju Yeon

PY - 2014

Y1 - 2014

N2 - In manufacturing, an enormous amount of data, both real and simulation data, is being continuously generated. The appropriate information, if extracted from big data, could provide insights on increasing sustainability, productivity, flexibility, and competitive advantages and eventually contribute to achieving the objectives of smart manufacturing on agility, asset utilization, and sustainability. The challenge is to reduce information overload for manufacturing and filter useful information to get the same detail level of manufacturing insights. The adoption of manufacturing data analytics in a timely manner can facilitate moving traditional manufacturing to agile, and eventually, smart manufacturing. This paper addresses how to apply a standardized predictive modeling technique onto manufacturing data analytics applications.

AB - In manufacturing, an enormous amount of data, both real and simulation data, is being continuously generated. The appropriate information, if extracted from big data, could provide insights on increasing sustainability, productivity, flexibility, and competitive advantages and eventually contribute to achieving the objectives of smart manufacturing on agility, asset utilization, and sustainability. The challenge is to reduce information overload for manufacturing and filter useful information to get the same detail level of manufacturing insights. The adoption of manufacturing data analytics in a timely manner can facilitate moving traditional manufacturing to agile, and eventually, smart manufacturing. This paper addresses how to apply a standardized predictive modeling technique onto manufacturing data analytics applications.

KW - Data analytics

KW - Manufacturing

KW - PMML

KW - Predictive model

UR - https://www.scopus.com/pages/publications/84910138833

M3 - Conference contribution

AN - SCOPUS:84910138833

T3 - Fall Simulation Interoperability Workshop, 2014 Fall SIW

SP - 113

EP - 117

BT - Fall Simulation Interoperability Workshop, 2014 Fall SIW

PB - SISO - Simulation Interoperability Standards Organization

T2 - Fall Simulation Interoperability Workshop, 2014 Fall SIW

Y2 - 8 September 2014 through 12 September 2014

ER -

PMML in manufacturing applications

Abstract

Publication series

Conference

Keywords

UN SDGs

Other files and links

Fingerprint

Cite this