Taxonomy of a Fast Data Business Model in the Mobility Market

Contents

1 Introduction

1.1 Problem Statement

1.2 Motivation

1.3 Background

1.4 Methodology

1.5 Scope and Outline of this Thesis

2 From Big to Fast Data

2.1 Defining Big Data

2.2 Big Data Related Challenges

2.3 Fast Data

3 Decision Tree Algorithms

3.1 A Short Introduction to Decision Trees

3.2 Classification Trees

3.2.1 ID3

3.2.2 C4.5

3.2.3 CART

3.3 Decision Trees and Fast Data

3.3.1 Hoeffding Tree

3.3.2 VFDT

3.3.3 CVFDT

3.3.4 UCVFDT

4 Preliminary Business Model

4.1 Brief Introduction to Open Innovation

4.2 Step-By-Step to the Business Model Concept

5 Explorative Research

5.1 Case Studies

5.2 Expert Interviews

5.3 Interview - Oskar Dohrau

5.4 Interview - Jürgen Götzenauer

6 Descriptive Research

7 Business Model Adaption

8 Conclusion and Future Work

Research Objectives and Themes

The thesis aims to develop a taxonomy for a business model focused on Open Innovation, specifically tailored for the development of decision tree algorithms for real-time data analysis in the mobility sector.

• Transition from Big Data to Fast Data processing paradigms.
• Theoretical analysis and taxonomy of Decision Tree algorithms.
• Application of the Business Model Canvas in an Open Innovation context.
• Explorative research through case studies and expert interviews within the mobility industry.
• Adaptation of business models to support autonomous driving and high-frequency data requirements.

Excerpt from the Book

Summary of Chapters

1 Introduction: This chapter introduces the problem definition, motivations, and background of the thesis, as well as the research methodology used to achieve the research objectives.

2 From Big to Fast Data: This chapter introduces the origins, the terms and concepts of big data and defines what is meant by real-time data processing and fast data.

3 Decision Tree Algorithms: This chapter gives an overview of decision trees used for classification and selection in real-time data analysis.

4 Preliminary Business Model: This chapter introduces the major contribution of this thesis: the concept of the business model, with a detailed explanation of the fundamentals of open innovation.

5 Explorative Research: This chapter outlines the research done using case studies and interviews.

6 Descriptive Research: This chapter summarizes the results of a questionnaire addressed to managers and employees of local RTD industries.

7 Business Model Adaption: This chapter covers the adaptation of the business model concept, based on the findings from interviews, case studies, and the questionnaire.

8 Conclusion and Future Work: This chapter provides an outlook on possible future research and presents some final conclusions.

Keywords

Decision Tree, Open Innovation, Business Model Canvas, Serious Gaming, Automotive, Big Data, Fast Data, Data Science, Artificial Intelligence, Autonomous Driving, Streaming Data, Machine Learning, Data Mining, Knowledge Transfer, Mobility Sector

Frequently Asked Questions

What is the core subject of this thesis?

The thesis explores the development of a taxonomy for an Open Innovation-based business model, specifically designed for creating decision tree algorithms for real-time data analysis in the automotive and mobility sector.

What are the central thematic fields?

The main themes include Big Data, Fast Data streaming models, Decision Tree classification algorithms, and the application of Open Innovation strategies within the automotive mobility market.

What is the primary research question?

The research asks: How does the taxonomy of an open innovation based business model of developing decision tree algorithms for the use in generic real-time data analysis in the mobility sector look like?

Which scientific methodology is applied?

The work utilizes a mixed-method approach including literature research, conceptual model building, case studies, and expert interviews with industry professionals.

What is covered in the main body?

The main body moves from the theoretical foundations of streaming data and decision tree algorithms to the practical design and evaluation of a business model adapted for future autonomous driving technologies.

Which keywords characterize the work?

Key terms include Decision Tree, Open Innovation, Business Model Canvas, Fast Data, and Autonomous Driving.

Why are "Serious Games" relevant to this business model?

The research proposes that serious games can function as virtual testing grounds to collect real-time data and train artificial intelligence for autonomous driving systems.

How do expert interviews influence the business model?

Expert interviews provide practical validation from the industry, specifically helping to identify stakeholder needs, such as the requirement for standardized security and data management protocols in autonomous vehicle development.