Sharing Economy and Big Data Analytics

Soraya Sedkaoui is a Senior Lecturer at the University of Khemis Miliana, Algeria, as well as a data analyst and a strategic consultant. Her interests include Big Data, and the development of algorithms and models for business applications.Mounia Khelfaoui is a Researcher and Maître de conférences at the University of Khemis Miliana. Her research mainly focuses on sustainable development and all the relevant themes, with a focus on the social responsibility of organizations.

• Preface xiIntroduction xiiiPart 1. The Sharing Economy or the Emergence of a New Business Model 1Chapter 1. The Sharing Economy: A Concept Under Construction 31.1. Introduction 31.2. From simple sharing to the sharing economy 51.2.1. The genesis of the sharing economy and the break with “consumer” society 51.2.2. The sharing economy: which economy? 81.3. The foundations of the sharing economy 101.3.1. Peer-to-peer (P2P): a revolution in computer networks 101.3.2. The gift: the abstract aspect of the sharing economy 131.3.3. The service economy and the offer of use 181.4. Conclusion 24Chapter 2. An Opportunity for the Business World 252.1. Introduction 252.2. Prosumption: a new sharing economy trend for the consumer 272.3. Poverty: a target in the spotlight of the shared economy 292.4. Controversies on economic opportunities of the sharing economy 312.5. Conclusion 37Chapter 3. Risks and Issues of the Sharing Economy 393.1. Introduction 393.2. Uberization: a white grain or just a summer breeze? 403.3. The sharing economy: a disruptive model 433.4. Major issues of the sharing economy 473.5. Conclusion 50Chapter 4. Digital Platforms and the Sharing Mechanism 514.1. Introduction 514.2. Digital platforms: “What growth!” 524.3. Digital platforms or technology at the service of the economy 544.4. From the sharing economy to the sharing platform economy 574.5. Conclusion 59Part 2. Big Data Analytics at the Service of the Sharing Economy 61Chapter 5. Beyond the Word “Big”: The Changes 635.1. Introduction 635.2. The 3 Vs and much more: volume, variety, velocity 645.2.1. Volume 655.2.2. The variety 665.2.3. Velocity 675.2.4. What else? 685.3. The growth of computing and storage capacities 695.3.1. Big Data versus Big Computing 705.3.2. Big Data storage 715.3.3. Updating Moore’s Law 735.4. Business context change in the era of Big Data 745.4.1. The decision-making process and the dynamics of value creation 755.4.2. The emergence of new data-driven business models 775.5. Conclusion 78Chapter 6. The Art of Analytics 816.1. Introduction 816.2. From simple analysis to Big Data analytics 826.2.1. Descriptive analysis: learning from past behavior to influence future outcomes 846.2.2. Predictive analysis: analyzing data to predict future outcomes 846.2.3. Prescriptive analysis: recommending one or more action plan(s) 856.2.4. From descriptive analysis to prescriptive analysis: an example 876.3. The process of Big Data analytics: from the data source to its analysis 886.3.1. Definition of objectives and requirements 906.3.2. Data collection 916.3.3. Data preparation 926.3.4. Exploration and interpretation 946.3.5. Modeling 956.3.6. Deployment 976.4. Conclusion 97Chapter 7. Data and Platforms in the Sharing Context 997.1. Introduction 997.2. Pioneers in Big Data 1017.2.1. Big Data on Walmart’s shelves 1017.2.2. The Big Data behind Netflix’s success story 1027.2.3. The Amazon version of Big Data 1037.2.4. Big data and social networks: the case of Facebook 1047.2.5. IBM and data analysis in the health sector 1057.3. Data, essential for sharing 1067.3.1. Data and platforms at the heart of the sharing economy 1087.3.2. The data of sharing economy companies 1107.3.3. Privacy and data security in a sharing economy 1117.3.4. Open Data and platform data sharing 1147.4. Conclusion 116Chapter 8. Big Data Analytics Applied to the Sharing Economy 1198.1. Introduction 1198.2. Big Data and Machine Learning algorithms serving the sharing economy 1218.2.1. Machine Learning algorithms 1228.2.2. Algorithmic applications in the sharing economy context 1248.3. Big Data technologies: the sharing economy companies’ toolbox 1258.3.1. The appearance of a new concept and the creation of new technologies 1278.4. Big Data on the agenda of sharing economy companies 1308.4.1. Uber 1318.4.2. Airbnb 1328.4.3. BlaBlaCar 1338.4.4. Lyft 1348.4.5. Yelp 1358.4.6. Other cases 1378.5. Conclusion 139Part 3. The Sharing Economy? Not Without Big Data Algorithms 141Chapter 9. Linear Regression 1439.1. Introduction 1439.2. Linear regression: an advanced analysis algorithm 1449.2.1. How are regression problems identified? 1459.2.2. The linear regression model 1469.2.3. Minimizing modeling error 1489.3. Other regression methods 1499.3.1. Logistic regression 1509.3.2. Additional regression models: regularized regression 1519.4. Building your first predictive model: a use case 1529.4.1. What variables help set a rental price on Airbnb? 1529.5. Conclusion 169Chapter 10. Classification Algorithms 17110.1. Introduction 17110.2. A tour of classification algorithms 17210.2.1. Decision trees 17210.2.2. Naïve Bayes 17510.2.3. Support Vector Machine (SVM) 17710.2.4. Other classification algorithms 17910.3. Modeling Airbnb prices with classification algorithms 18310.3.1. The work that’s already been done: overview 18410.3.2. Models based on trees: decision tree versus Random Forest 18510.3.3. Price prediction with kNN 19010.4. Conclusion 193Chapter 11. Cluster Analysis 19511.1. Introduction 19511.2. Cluster analysis: general framework 19611.2.1. Cluster analysis applications 19711.2.2. The clustering algorithm and the similarity measure 19811.3. Grouping similar objects using k-means 20011.3.1. The k-means algorithm 20111.3.2. Determine the number of clusters 20311.4. Hierarchical classification 20511.4.1. The hierarchical model approach 20611.4.2. Dendrograms 20711.5. Discovering hidden structures with clustering algorithms 20811.5.1. Illustration of the classification of prices based on different characteristics using the k-means algorithm 20911.5.2. Identify the number of clusters k 21011.6. Conclusion 213Conclusion 215References 217Index 233