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Aggregation and Disaggregation of In...

Chen, Yuyue.

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Aggregation and Disaggregation of Information: A Holistic View.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Aggregation and Disaggregation of Information: A Holistic View./
Author:	Chen, Yuyue.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	83 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-01, Section: A.
Contained By:	Dissertations Abstracts International83-01A.
Subject:	Business administration. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28547887
ISBN:	9798516962295

Aggregation and Disaggregation of Information: A Holistic View.
Chen, Yuyue.

Aggregation and Disaggregation of Information: A Holistic View. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 83 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: A.

Thesis (Ph.D.)--Drexel University, 2021.

This item must not be sold to any third party vendors.

This dissertation develops optimization solutions to tackle two challenges in data mining: intelligence aggregation and signal disaggregation. For each challenge, our solution consists of an optimization model and an efficient and robust algorithm to solve it.We first present a model and a solution algorithm that can decompose aggregated information into smaller constituents for multiple systems. For instance, our approach simultaneously disaggregates integrated energy signals from houses into specific measurements of appliances/activities. The results provide managerial insights for energy services without costly installing additional hardware. Unlike some traditional approaches in the literature that require large training datasets, our disaggregation algorithm uses contextual features as inputs combined with a transfer learning approach to improve model accuracy and interpretation.Next, we propose a new optimization approach to aggregating information from multiple information sources. Our model is solved with an alternated quadratic programming optimization and has been evaluated to accurately predict a stock's future trading actions by capturing inter-correlations and quantifying the reliability scores of analysts' recommendations. We further extend our aggregation model with additional time-related relevance features. The extension allows us to improve the prediction accuracy of stock future performance as well as to better understand the impact of time-dimension on aggregation tasks.Although disaggregation is the inverse process of aggregation, we investigate both topics by incorporating various types of inter-correlations in complex systems. The optimization models in this dissertation can be further applied to other research fields such as healthcare, airline business, marketing, and operations management.

ISBN: 9798516962295Subjects--Topical Terms:

3168311
Business administration.
Subjects--Index Terms:

Context-aware learning

Aggregation and Disaggregation of Information: A Holistic View.
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100 1 $a Chen, Yuyue. $0 (orcid)0000-0002-5726-9030 $3 3563980
245 1 0 $a Aggregation and Disaggregation of Information: A Holistic View.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 83 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-01, Section: A.
500 $a Advisor: Benson, Hande.
502 $a Thesis (Ph.D.)--Drexel University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation develops optimization solutions to tackle two challenges in data mining: intelligence aggregation and signal disaggregation. For each challenge, our solution consists of an optimization model and an efficient and robust algorithm to solve it.We first present a model and a solution algorithm that can decompose aggregated information into smaller constituents for multiple systems. For instance, our approach simultaneously disaggregates integrated energy signals from houses into specific measurements of appliances/activities. The results provide managerial insights for energy services without costly installing additional hardware. Unlike some traditional approaches in the literature that require large training datasets, our disaggregation algorithm uses contextual features as inputs combined with a transfer learning approach to improve model accuracy and interpretation.Next, we propose a new optimization approach to aggregating information from multiple information sources. Our model is solved with an alternated quadratic programming optimization and has been evaluated to accurately predict a stock's future trading actions by capturing inter-correlations and quantifying the reliability scores of analysts' recommendations. We further extend our aggregation model with additional time-related relevance features. The extension allows us to improve the prediction accuracy of stock future performance as well as to better understand the impact of time-dimension on aggregation tasks.Although disaggregation is the inverse process of aggregation, we investigate both topics by incorporating various types of inter-correlations in complex systems. The optimization models in this dissertation can be further applied to other research fields such as healthcare, airline business, marketing, and operations management.
590 $a School code: 0065.
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650 4 $a Information science. $3 554358
650 4 $a Crowdsourcing. $3 3377825
650 4 $a Information sources. $3 3560861
650 4 $a Households. $3 579926
650 4 $a Decision making. $3 517204
653 $a Context-aware learning
653 $a Crowd-sourced Intelligence
653 $a Decision aggregation
653 $a Energy disaggregation
653 $a Graph ensemble
653 $a Transfer learning
653 $a Data mining
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690 $a 0454
690 $a 0723
710 2 $a Drexel University. $b Business Administration (LeBow College of Business). $3 2094586
773 0 $t Dissertations Abstracts International $g 83-01A.
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792 $a 2021
793 $a English
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