Today, the market around mobility is under heavy change. The industry is preparing the change from fossil fuels to their alternatives. Large funds are raised to build new products based on electricity, either with rechargeable battery packs or solar energy. Another field of research and development is autonomous driving. Google and Tesla are prominent examples of companies developing technologies and prototypes of future autonomous cars. The information technology used in such cars is using data analysis and artificial intelligence to observe, measure, predict and control traffic situations. This technology is influenced by one of the trending technologies of the early 21th century, Big Data.
The basic research in data analysis had its crucial test in many Big Data related projects and products, like Facebook and Twitter. A part of the data analysis process are classification algorithms used to classify the input data for targeted further processing. Decision trees are one of the algorithms of classification. They are very precisely, but tend to overflow. This thesis will introduce the taxonomy of a business model, related to Open Innovation, for developing a new decision tree algorithm for the mobility market. Therefore, the data analysis process up to classification algorithms are introduced. The major decision tree algorithms are discussed in detail to build up the characteristics of the new decision tree algorithm.
Furthermore, the Open Innovation model is introduced to carve out the assets and drawbacks of this model. Based on that theoretical information, the taxonomy of a preliminary business model is developed and visualized using the business model canvas. To prove this concept, expert interviews and questionnaires were used to gather feedback and new ideas. The collected information is used to modify the taxonomy of the business model. The resulting business model canvas shows the possibility of implementation.
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
1.1 Problem Statement
The rapid growth of the global internet traffic is accelerated by the trends of Internet of Things (IoT) and Machine-to-Machine (M2M). They transmit data from various sensors, usage information and other data to their service endpoints. Additionally, autonomous transportation and other artificial intelligence systems are a major part of future’s data exchange applications.
The future data analysis tasks and intelligent services need great efforts to process these data with high accuracy in nearly real-time. The real-time predictions need to rely on decision making processes with a growing training data base and increasing complexity of data relations.
Because the amount of generated and stored data is rising worldwide, the number of data scientists has to grow likewise. The high demand on this data scientists is stated by Gartner’s 2015th hype cycle of emerging technologies, as shown in figure 1. The global requests for data science skills can be fulfilled by either educating more people in theories and practice of data science, or by developing fully automated software for multidisciplinary data processing.
Combining several branches of learning in software based artificial intelligence systems, needs a technological transition from traditional data processing to modern streaming data processing systems. Traditional data processing relies on historic data and time-intense data processing pipelines, which are not usable for real-time decision making processes. Modern data processing systems deal with real-time streaming data. They highly depend on trained algorithms providing decisions in milliseconds.
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.
- Quote paper
- Andreas Landgraf (Author), 2016, Taxonomy of a Fast Data Business Model in the Mobility Market, Munich, GRIN Verlag, https://www.grin.com/document/541425
