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Application of secondary analyses on...

Perez, Luis G.

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Application of secondary analyses on industrial data sets.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Application of secondary analyses on industrial data sets./
Author:	Perez, Luis G.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	123 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
Contained By:	Dissertation Abstracts International78-07B(E).
Subject:	Environmental engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10247889
ISBN:	9781369523157

Application of secondary analyses on industrial data sets.
Perez, Luis G.

Application of secondary analyses on industrial data sets. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 123 p.

Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.

Thesis (Ph.D.)--The University of Texas at El Paso, 2016.

Secondary analysis on quantitative data sets is the in-depth analysis of relationships, trends, patterns or behaviors that are not obvious from a superficial examination of data but that can be very germane in the application of that data. The present work presents a framework for investigators to use in applying secondary analysis on big data that correlates to the research topic. The framework can facilitate the illumination of possible data behaviors or patterns that could be useful in arriving at an answer to a question. Behavior of monitored equipment (analyzers, meters, etc.) can easily be depicted and can be used to indicate graphically how patterns in the data support or reject possible outcomes to a question.

ISBN: 9781369523157Subjects--Topical Terms:

548583
Environmental engineering.

Application of secondary analyses on industrial data sets.
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020 $a 9781369523157
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100 1 $a Perez, Luis G. $3 3287228
245 1 0 $a Application of secondary analyses on industrial data sets.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 123 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
500 $a Adviser: Peter Golding.
502 $a Thesis (Ph.D.)--The University of Texas at El Paso, 2016.
520 $a Secondary analysis on quantitative data sets is the in-depth analysis of relationships, trends, patterns or behaviors that are not obvious from a superficial examination of data but that can be very germane in the application of that data. The present work presents a framework for investigators to use in applying secondary analysis on big data that correlates to the research topic. The framework can facilitate the illumination of possible data behaviors or patterns that could be useful in arriving at an answer to a question. Behavior of monitored equipment (analyzers, meters, etc.) can easily be depicted and can be used to indicate graphically how patterns in the data support or reject possible outcomes to a question.
520 $a This present work illustrates the value of secondary analyses in three different case studies, where this approach is demonstrably used to discover behaviors in operational data of a large gas transmission pipeline, and in sanctioning air quality permit actions for an electric generating facility. The analyses performed provide great insight as to how decisions and responses to regulatory-related actions are being improved upon using big data sets. The tangible results of the application of secondary analysis in environmental science and engineering decision making, exemplified in these case studies, is presented as evidence of its intrinsic value. Moreover, the inherent value of this study is derived by the fact that the tools used to perform these analyses did not require expensive, complex resources.
520 $a It is shown that there is substantial, quantifiable value in applying the methods presented here for secondary analysis. Benefits can be quantified not only monetarily but also in improving operations by offering operational flexibility. Querying of available data stores through secondary analysis offers substantial opportunities for industry to gain insights and understanding into previously concealed databased relationships.
590 $a School code: 0459.
650 4 $a Environmental engineering. $3 548583
650 4 $a Environmental management. $3 535182
650 4 $a Engineering. $3 586835
690 $a 0775
690 $a 0474
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710 2 $a The University of Texas at El Paso. $b Environ. Sci. & Eng.. $3 3194056
773 0 $t Dissertation Abstracts International $g 78-07B(E).
790 $a 0459
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10247889