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Data science and productivity analytics / Vincent Charles, Juan Aparicio, Joe Zhu, editors.

Language: English Publication details: Cham : Springer, 2020.Description: x, 439 p. : ill. ; 24 cmISBN:
  • 9783030433864 (pbk)
DDC classification:
  • 519.5
Contents:
Intro -- Preface -- Contents -- 1 Data Envelopment Analysis and Big Data: Revisit with a Faster Method -- 1.1 Introduction -- 1.2 The Framework -- 1.3 A Basic Example -- 1.3.1 Applying the Framework Without Step 3 -- 1.3.2 Applying the Framework with Step 3 -- 1.4 A Theoretical Example -- 1.4.1 Generating Data -- 1.4.2 The Outcomes of BH -- 1.4.3 The Outcomes of HD -- 1.4.4 Comparison Between BH and the Framework -- 1.5 Uniform and Cobb-Douglas Approaches -- 1.5.1 Generating Data -- 1.5.2 The Outcome -- 1.6 Changing the Cardinality and Dimension -- 1.6.1 Generating Data
3.2 Characterising Decision Support Systems (DSS) -- 3.3 From Data Science to Decision-Making -- 3.3.1 Model-Driven and Data-Driven Approaches -- 3.3.2 Descriptive Versus Predictive Models -- 3.4 Principles of Classification Methods -- 3.4.1 The Type of Attributes -- 3.4.2 The First Decision Tree Algorithms -- 3.4.3 Measuring Accuracy (Confusion Matrices) -- 3.4.4 Generating and Reducing Rule Systems -- 3.5 Real Applications of Classification Methods -- 3.5.1 Predicting Customer Behaviour on Vehicle Reservations (Rent-a-Car Company)
3.5.2 Extracting Spending Patterns on Tourism (Tourism Valencian Agency) -- 3.5.3 Avoiding Unnecessary Pre-surgery Tests (Healthcare) -- 3.5.4 Classifying Violent/Radicalism on Twitter (Security Surveillance) -- 3.6 From Data Science to DSS in Four Scenarios -- 3.7 Opportunities for Future Research -- References -- 4 Identification of Congestion in DEA -- 4.1 Introduction -- 4.2 Preliminaries -- 4.2.1 Notation -- 4.2.2 The VRS Model -- 4.2.3 Efficiency -- 4.2.4 Finding a Maximal Element of a Non-negative Polyhedral Set -- 4.3 Congestion of Output-Efficient DMUs
4.3.1 General Definition of Input Congestion -- 4.3.2 The Congestion Technology -- 4.3.3 Weak and Strong Congestions -- 4.3.4 The Congestion Model -- 4.3.5 The Congestion-Identification Model -- 4.4 Congestion of Output-Inefficient DMUs -- 4.4.1 Congestion of Faces of Technology mathcalTCONG -- 4.4.2 The Minimal Face of an Output-Inefficient DMU -- 4.4.3 A Precise Definition of Congestion for Output-Inefficient DMUs -- 4.5 Three Congestion-Identification Algorithms -- 4.5.1 Algorithm 1: Incorporating the Non-negativity Assumption -- 4.5.2 Algorithm 2: Enhancing Computational Efficiency
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Holdings
Item type Current library Collection Call number Status Date due Barcode
Book Book Library of People's Majlis General/ Lending General G-EN 519.5 DAT (Browse shelf(Opens below)) Available 0000002954

4.5.3 Algorithm 3: Removing the Non-negativity Assumption

Intro -- Preface -- Contents -- 1 Data Envelopment Analysis and Big Data: Revisit with a Faster Method -- 1.1 Introduction -- 1.2 The Framework -- 1.3 A Basic Example -- 1.3.1 Applying the Framework Without Step 3 -- 1.3.2 Applying the Framework with Step 3 -- 1.4 A Theoretical Example -- 1.4.1 Generating Data -- 1.4.2 The Outcomes of BH -- 1.4.3 The Outcomes of HD -- 1.4.4 Comparison Between BH and the Framework -- 1.5 Uniform and Cobb-Douglas Approaches -- 1.5.1 Generating Data -- 1.5.2 The Outcome -- 1.6 Changing the Cardinality and Dimension -- 1.6.1 Generating Data

3.2 Characterising Decision Support Systems (DSS) -- 3.3 From Data Science to Decision-Making -- 3.3.1 Model-Driven and Data-Driven Approaches -- 3.3.2 Descriptive Versus Predictive Models -- 3.4 Principles of Classification Methods -- 3.4.1 The Type of Attributes -- 3.4.2 The First Decision Tree Algorithms -- 3.4.3 Measuring Accuracy (Confusion Matrices) -- 3.4.4 Generating and Reducing Rule Systems -- 3.5 Real Applications of Classification Methods -- 3.5.1 Predicting Customer Behaviour on Vehicle Reservations (Rent-a-Car Company)

3.5.2 Extracting Spending Patterns on Tourism (Tourism Valencian Agency) -- 3.5.3 Avoiding Unnecessary Pre-surgery Tests (Healthcare) -- 3.5.4 Classifying Violent/Radicalism on Twitter (Security Surveillance) -- 3.6 From Data Science to DSS in Four Scenarios -- 3.7 Opportunities for Future Research -- References -- 4 Identification of Congestion in DEA -- 4.1 Introduction -- 4.2 Preliminaries -- 4.2.1 Notation -- 4.2.2 The VRS Model -- 4.2.3 Efficiency -- 4.2.4 Finding a Maximal Element of a Non-negative Polyhedral Set -- 4.3 Congestion of Output-Efficient DMUs

4.3.1 General Definition of Input Congestion -- 4.3.2 The Congestion Technology -- 4.3.3 Weak and Strong Congestions -- 4.3.4 The Congestion Model -- 4.3.5 The Congestion-Identification Model -- 4.4 Congestion of Output-Inefficient DMUs -- 4.4.1 Congestion of Faces of Technology mathcalTCONG -- 4.4.2 The Minimal Face of an Output-Inefficient DMU -- 4.4.3 A Precise Definition of Congestion for Output-Inefficient DMUs -- 4.5 Three Congestion-Identification Algorithms -- 4.5.1 Algorithm 1: Incorporating the Non-negativity Assumption -- 4.5.2 Algorithm 2: Enhancing Computational Efficiency

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