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Automated Support for Model Selectio...

Missakian, Mario Sarkis.

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Automated Support for Model Selection Using Analytic Hierarchy Process.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Automated Support for Model Selection Using Analytic Hierarchy Process./
作者:	Missakian, Mario Sarkis.
面頁冊數:	218 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .
Contained By:	Dissertation Abstracts International72-07B.
標題:	Information Technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3454450
ISBN:	9781124638188

Automated Support for Model Selection Using Analytic Hierarchy Process.
Missakian, Mario Sarkis.

Automated Support for Model Selection Using Analytic Hierarchy Process. - 218 p.

Source: Dissertation Abstracts International, Volume: 72-07, Section: B, page: .

Thesis (Ph.D.)--Nova Southeastern University, 2011.

Providing automated support for model selection is a significant research challenge in model management. Organizations maintain vast growing repositories of analytical models, typically in the form of spreadsheets. Effective reuse of these models could result in significant cost savings and improvements in productivity. However, in practice, model reuse is severely limited by two main challenges: (1) lack of relevant information about the models maintained in the repository, and (2) lack of end user knowledge that prevents them from selecting appropriate models for a given problem solving task.

ISBN: 9781124638188Subjects--Topical Terms:

1030799
Information Technology.

Automated Support for Model Selection Using Analytic Hierarchy Process.
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500 $a Adviser: Sumitra Mukherjee.
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520 $a Providing automated support for model selection is a significant research challenge in model management. Organizations maintain vast growing repositories of analytical models, typically in the form of spreadsheets. Effective reuse of these models could result in significant cost savings and improvements in productivity. However, in practice, model reuse is severely limited by two main challenges: (1) lack of relevant information about the models maintained in the repository, and (2) lack of end user knowledge that prevents them from selecting appropriate models for a given problem solving task.
520 $a This study built on the existing model management literature to address these research challenges. First, this research captured the relevant meta-information about the models. Next, it identified the features based on which models are selected. Finally, it used Analytic Hierarchy Process (AHP) to select the most appropriate model for any specified problem. AHP is an established method for multi-criteria decision-making that is suitable for the model selection task. To evaluate the proposed method for automated model selection, this study developed a simulated prototype system that implemented this method and tested it in two realistic end-user model selection scenarios based on previously benchmarked test problems.
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