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Data Architecture to Quantify and Characterize the Resilience of a Natural Disaster-Affected System.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Architecture to Quantify and Characterize the Resilience of a Natural Disaster-Affected System./
作者:	Laun, Alexander Walter.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	116 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
標題:	Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28861328
ISBN:	9798496539609

Data Architecture to Quantify and Characterize the Resilience of a Natural Disaster-Affected System.
Laun, Alexander Walter.

Data Architecture to Quantify and Characterize the Resilience of a Natural Disaster-Affected System. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 116 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (Ph.D.)--The George Washington University, 2022.

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

For practitioners of systems engineering, designing for resilience requires one to consider how to actually measure a disrupted system's resilient performance. When subjected to an unexpected disruption, these engineered systems are expected to anticipate, respond to, recover from, and adapt to a given event. Calculable measures of system performance can only be derived from fully operationalized data, information, and knowledge. This dissertation considers the data-information-knowledge hierarchy from the perspective of more effectively assessing the resilient rebound behavior of a natural disaster-affected system. Unfortunately, for such systems, real-world performance data are too commonly sparse, unstructured, poorly organized, and/or lacking in context. Leveraging technical insights from the systems engineering, resilience engineering, and data/information management communities, this dissertation presents a feasible data architecture and corresponding conceptual data model to improve resilience-centric data collection, processing, use, and storage activities. Common architecture frameworks, data structures, and data processes are all detailed and evaluated. The proposed data architecture, representing the information domain, accounts for unique system, operating environment, and disruption views, including artifacts to promote knowledge management and learning. Three case studies related to Hurricane Ike, Hurricane Irma, and Hurricane Delta, respectively, are employed to validate the overall data architecting and modeling approach. Ultimately, this dissertation offers a concise methodology to better transform disparate real-world data into meaningful measures of system resilience.

ISBN: 9798496539609Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Data architecture

Data Architecture to Quantify and Characterize the Resilience of a Natural Disaster-Affected System.
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