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Titlebook: Data-Enabled Analytics; DEA for Big Data Joe Zhu,Vincent Charles Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive

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樓主
發(fā)表于 2025-3-21 17:51:37 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data-Enabled Analytics
副標題DEA for Big Data
編輯Joe Zhu,Vincent Charles
視頻videohttp://file.papertrans.cn/264/263324/263324.mp4
概述Explores novel uses of Data Envelopment Analysis and Big Data.Introduces DEA as a data mining tool, under the big data umbrella.Exams DEA models beyond their present scope and mine new insights for be
叢書名稱International Series in Operations Research & Management Science
圖書封面Titlebook: Data-Enabled Analytics; DEA for Big Data Joe Zhu,Vincent Charles Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive
描述This book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.
出版日期Book 2021
關(guān)鍵詞efficiency; big data; best-practice; data envelopment analysis; data-enabled analytics; data science; fore
版次1
doihttps://doi.org/10.1007/978-3-030-75162-3
isbn_softcover978-3-030-75164-7
isbn_ebook978-3-030-75162-3Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 21:44:26 | 只看該作者
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發(fā)表于 2025-3-22 01:01:29 | 只看該作者
Book 2021ers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.
地板
發(fā)表于 2025-3-22 07:55:16 | 只看該作者
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0884-8289 A has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.978-3-030-75164-7978-3-030-75162-3Series ISSN 0884-8289 Series E-ISSN 2214-7934
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Magda Pischetola,Lone Dirckinck-Holmfeldractice. The network paradigm provides a versatile and efficient modelling framework that allows computing a wide array of quantitative characterization measures as well as powerful visualization capabilities. The methodology is illustrated with data on how the COVID-19 pandemic has affected the different countries.
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發(fā)表于 2025-3-23 02:47:07 | 只看該作者
Network DEA and Big Data with an Application to the Coronavirus Pandemic,rable and undesirable outputs, and the reallocation of inputs, across time, to optimize production. An example of the use of NDEA is offered for the Covid Pandemic in the US. We find that an optimal reallocation of tests for Covid could have averted 10,800 deaths.
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發(fā)表于 2025-3-23 08:56:53 | 只看該作者
Hierarchical Data Envelopment Analysis for Classification of High-Dimensional Data,d security attributes. Furthermore, the findings can be used to assist countries in projecting learning path from other high-performing nations. Such path information doesn’t exist when country grouping is carried out using personal judgement thus reduces subjectivity in measuring multiple food security performance attributes.
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