The Monetization of Technical Data: Innovations from Industry and Research

Greetings from Minister Prof. Dr. Andreas Pinkwart
Greetings from Dorothee Bär
Monetization of data—Foreword
Contents
1 Monetization of Data at the Example of Manufacturing Machines
	Abstract
	1.1	Introduction
	1.2	Basics
		1.2.1	What is Data Monetization?
		1.2.2	Company-centered, Internal Added Value
		1.2.3	Customer-centered, External Value
	1.3	Challenges and Solutions
	1.4	The Path to Data Monetization
		1.4.1	Framework for Data Monetization
		1.4.2	 Use Cases
			1.4.2.1 Use Case 1: Trading of Machine Data
			1.4.2.2 Use Case 2: Data-driven Assistant System
			1.4.2.3 Use Case 3: Transform Business Model
	1.5	Conclusion
	References
Part I Legal aspects of Data Monetization
2 Data Monetization in Law
	Abstract
	2.1	Classification of the Observed Monetization Effects
	2.2	Current Importance
	2.3	Guide for the Commission for the Joint Use of Data from the Private Sector
		2.3.1	Recorded Variants
		2.3.2	Contractual Basis
		2.3.3	Legal Principles—Connected with Competition Law
			2.3.3.1 Transparency
			2.3.3.2 Joint Value Creation
			2.3.3.3 Protection of the Business Interests of All Parties
			2.3.3.4 Unadulterated Competition
			2.3.3.5 Data Independence
		2.3.4	Extensions
	2.4	“Property” of the Data
		2.4.1	Previous Indications
		2.4.2	Sampling Judgment of the European Court of Justice
			2.4.2.1 Connection with Digitization
	2.5	Competition Law Access Claims
		2.5.1	Platform Companies
		2.5.2	Modification Due to Climate Protection?
	2.6	Obligation to Pay a Reasonable Fee
		2.6.1	Assessment According to the Innovative Character of the Performance
		2.6.2	Prohibition of Discount Systems
		2.6.3	Injunction Under the Ruling Huawei
	2.7	Standardization by Associations
	2.8	Corporate Cooperation
	2.9	Information Exchange
	2.10	Summary
	References
3 Monetization of Technical Data using the Example of Technical Employee Data
	Abstract
	3.1	Introduction
	3.2	Basics and Methods
		3.2.1	Employer’s Monitoring Right
		3.2.2	Works Agreement
		3.2.3	Consent
		3.2.4	Interim Result
	3.3	Challenges and Solutions
		3.3.1	Prohibition of Coupling
			3.3.1.1 Applicability of the Prohibition of Coupling
			3.3.1.2 Violation of the Prohibition of Coupling
			3.3.1.3 Consequence of the Violation
			3.3.1.4 Interim Result
		3.3.2	Excessive Incentives
	3.4	Examples
		3.4.1	Examples of Technical Employee Data
		3.4.2	Examples of Economic Advantages
	3.5	Summary
	References
4 The enforcement and bankruptcy of blockchain-based assets (Crypto-Assets)
	Abstract
	4.1	Introduction
	4.2	Basics of blockchain-based values
		4.2.1	Blockchain technology at a glance
			4.2.1.1 Basic structure of a blockchain
			4.2.1.2 Continuation of the blockchain
			4.2.1.3 Types of blockchain applications
		4.2.2	Crypto-assets as a new form of digital assets
	4.3	Enforceability of the law
		4.3.1	Individual enforcement according to the ZPO
			4.3.1.1 Enforcement in token credits due to a money claim
			4.3.1.2 Enforcement of claims for transfer of crypto assets
			4.3.1.3 Enforcement of claims for transfer of crypto assets
			4.3.1.4 Jurisdiction of German enforcement authorities
			4.3.1.5 Practical challenges within nforcement
			4.3.1.6 Enforcement protection
		4.3.2	Bankcrupcy according to the InsO
			4.3.2.1 Current state of research
			4.3.2.2 Insolvency procedure
			4.3.2.3 Exclusion and segregation rights
			4.3.2.4 Practical challenges within the scope of enforcement
			4.3.2.5 Result of a more detailed insolvency law examination
	4.4	Summary and outlook
	References
Part II Business aspects of Data Monetization
5 Silence is Silver, Speech is Gold: The Benefits of Machine Learning and Text Analysis in the Financial Sector
	Abstract
	5.1	Current Relevance of Machine Learning in Finance
	5.2	Applications Within the Financial Sector
		5.2.1	Research
		5.2.2	Insurance Companies
		5.2.3	Banks
	5.3	Text Analysis and Machine Learning in the Financial Sector
		5.3.1	Procedure and Methodology
		5.3.2	Benefits for Practice
	5.4	Conclusion
	References
6 Monetization of Machine-generated Online Data — Cross-industry Opportunities and Challenges
	Abstract
	6.1	Introduction
	6.2	Basics and Methods
		6.2.1	Machine-generated Online Data: Definition and Relevance
		6.2.2	Data Sources and Data Usage
	6.3	Opportunities Through the Monetization of Machine-generated Online Data
	6.4	Trends and Outlook
	6.5	Summary
	References
7 Monetary Valuation of Data in the Context of Accounting
	Abstract
	7.1	Introduction
	7.2	Basics and Methods
		7.2.1	Properties of Data
		7.2.2	Valuation Approaches
	7.3	Challenges and Solutions
	7.4	Application to Technical Data Sets
	7.5	Discussion and Outlook
	References
8 How Can the German Mittelstand Embrace Digital Transformation?—Considerations on Data Products, Business Models and Platform Economics
	Abstract
	8.1	Digital Challenges for Industry
	8.2	How Can Data be Monetized?
	8.3	Development of Smart Services
	8.4	Innovation Through Business Models
	8.5	Research Competition for AI Solutions
	8.6	AI-based Solution Approach
	8.7	Use Cases in the AI Service-Meister Project
		8.7.1	Monitoring Remote States
		8.7.2	Accelerating Service Processes
		8.7.3	Recognizing Anomalies
		8.7.4	Diagnosing Problems Automatically, Reducing Maintenance Costs
		8.7.5	Monitoring Production Processes, Preventing Downtime
		8.7.6	Planning Deployments, Procuring Spare Parts
	8.8	What Changes can be Expected in the Area of Industry 4.0 Through Gaia-X?
	8.9	What is the Significance of Gaia-X for the Service-Meister Project?
	8.10	A Solution, Not Only for Technical Service
	References
9 Data Monetization Strategies for Manufacturing Companies
	Abstract
	9.1	Classification of the Observed Monetization Effects
	9.2	Introduction
	9.3	Difficulties of Data Monetization
	9.4	Strategies for Data Monetization
	9.5	Practical Example
	9.6	Discussion and Outlook
	References
Part III Information technology aspects of Data Monetization
10 End-to-End Architectures for Data Monetization in the Industrial Internet of Things (IIoT)
	Abstract
	10.1	Introduction
	10.2	Architecture
		10.2.1	Problem Overview
		10.2.2	Solution Approaches
		10.2.3	End-to-end-IoT
	10.3	Information Processing
		10.3.1	IoT Platform
			10.3.1.1 Definition
			10.3.1.2 Functions and Capabilities
			10.3.1.3 Support for IoT Applications
		10.3.2	Information Processing at the Edge
			10.3.2.1 Drivers
			10.3.2.2 The Edge Continuum
			10.3.2.3 Use Case 1—IoT Gateways
			10.3.2.4 Use Case 2—Edge-Analytics
		10.3.3	Non-functional Requirements
			10.3.3.1 Scalability
			10.3.3.2 Multi-tenancy
			10.3.3.3 Deployment Options ()
	10.4	Information Acquisition
		10.4.1	IoT Gateways and Other Architecture Patterns
		10.4.2	Low Power Devices
	10.5	Information Allocation and Distribution
		10.5.1	Lambda Architecture
		10.5.2	API Management
	10.6	Discussion
	10.7	Summary
	References
11 The Technology IOTA as an Open Infrastructure for Micro-Payments, IoT Communication, and Global Digital Security
	Abstract
	11.1	Classification of the Observed Monetization Effects
	11.2	Introduction
	11.3	What is IOTA?
	11.4	The Disadvantages of Traditional Blockchain
	11.5	IOTA as an Infrastructure for People, Organizations and the Internet of Things
	11.6	Micro-Payments for Data
	11.7	Microtransactions and Data Sharing as the Basis for New Business Models
	11.8	Requirements for the Capture and Monetization of Technical Data
	11.9	Data Management in IOTA—IOTA Streams
	11.10	Summary
	References
12 Data Notary—Auditable Data as a Basis for Monetization
	Abstract
	12.1	Introduction
		12.1.1	Digital Seal for Data
		12.1.2	Data Notary as a Driver for Data Monetization
	12.2	Technical Basics and Requirements
		12.2.1	Security ≠ Checking by Evidence
		12.2.2	Data Authenticity
		12.2.3	Data Integrity
		12.2.4	Raw Data vs. Auditable Data Assets
		12.2.5	Secure Storage of Evidence
	12.3	Data Notary Architecture
		12.3.1	Pillars of a Data Notary Service
			12.3.1.1 Source
			12.3.1.2 Service Source
			12.3.1.3 Data Storage Source
			12.3.1.4 Service Consumer
			12.3.1.5 Data Storage Consumer
			12.3.1.6 Consumer
			12.3.1.7 Memory
	12.4	Data Notary in Practical Use
		12.4.1	Company Profiles
		12.4.2	Problem Description
		12.4.3	Solution Approach—Auditable Machine Data
		12.4.4	Integration as an Extension to the Industrial IoT Platform
		12.4.5	Data Provision
		12.4.6	Audit and Use of Data
	12.5	Conclusion
	References
13 Secure, Verifiable Object Identities as Enablerfor Value Creation in Distributed Networks
	Abstract
	13.1	Classification of the Observed Monetization Effects
	13.2	Introduction
	13.3	Basics and Methods
		13.3.1	Attribute-Based Identity Model
		13.3.2	Zero-Knowledge Proof
		13.3.3	Digital Object Memory
	13.4	Architecture and Attack Description
		13.4.1	Architectural Description of a Zero-Knowledge Proof for Attributes of Objects
		13.4.2	Attacks on the System
	13.5	Discussion and Case Studies
	13.6	Summary
	References
Part IV Data Monetization in the Manufacturing Industry
14 Putting a Price Tag on Data
	Abstract
	14.1	Classification of the Observed Monetization Effects
	14.2	Introduction
	14.3	Vision of MyDataEconomy
	14.4	Architecture of MyDataEconomy
		14.4.1	Marketplace
		14.4.2	Network Nodes
		14.4.3	Client
		14.4.4	Fundamentals of the Decentralized Service Landscape
		14.4.5	Scalability of a Global Service and Data Alliance
	14.5	Examples of Applications
		14.5.1	Condition Monitoring
			14.5.1.1 Process Monitoring During Fineblanking
			14.5.1.2 Environmental monitoring during fineblanking
		14.5.2	Advanced Analytics
			14.5.2.1 Predictive Maintenance with the aid of structure-borne ound
		14.5.3	Monetization of Assets and Services
			14.5.3.1 Asset Monetization
			14.5.3.2 Service Monetization
	14.6	Summary
	References
15 The Internet of Production as the Foundation of Data Utilization in Production
	Abstract
	15.1	Classification of the Observed Monetization Effects
	15.2	Introduction
	15.3	Basics
		15.3.1	Internet of Production
		15.3.2	Digital Twin
		15.3.3	Digital Shadow
	15.4	Challenges and Solutions
		15.4.1	Challenges of Data-Driven Modeling
		15.4.2	Infrastructural Challenges
	15.5	Examples of the Digital Material Shadow
		15.5.1	In-Situ Material Classification using Artificial Neural Networks
		15.5.2	Interaction Effects as Digital Material Shadow in Process Chains
	15.6	Conclusion
	References
16 Graphic Approach to Energy Optimization through Artificial Intelligence
	Abstract
	16.1	Classification of the Observed Monetization Effects
	16.2	Introduction
	16.3	Basics and Methods
		16.3.1	Artificial Intelligence and Machine Learning
		16.3.2	Optimization
		16.3.3	Graphic Methods for Optimization
	16.4	Challenges and Solutions
	16.5	Optimization through Artificial Intelligence
		16.5.1	Process Understanding
			16.5.1.1 The Plant
			16.5.1.2 Retrofitting
			16.5.1.3 Variables and Process Diagrams
			16.5.1.4 Network
		16.5.2	Data acquisition and cleansing
		16.5.3	Creating an Optimization Strategy
		16.5.4	Optimization and Integration
	16.6	Results
	16.7	Outlook
	16.8	Conclusion
	References
17 Efficiency Increase through Data-Based Modeling of Quality and Production Cost Factors in the Nonwoven Industry
	Abstract
	17.1	Classification of the Observed Monetization Effects
	17.2	Introduction
	17.3	Basics and Methods
		17.3.1	Multi-Dimensional Optimization Methods
		17.3.2	Modeling Methods for Product Quality and Production Costs
	17.4	Challenges and Solutions
		17.4.1	Determination of the Influencing and Target Variables
			17.4.1.1 Target Variables
				17.4.1.1.1 Quality Parameters of the Carded Web
				17.4.1.1.2 Production Cost Parameters
			17.4.1.2 Influencing Factors
		17.4.2	Measuring the Parameters in Operation
		17.4.3	Data Preparation
			17.4.3.1 Labeling of Usable Data Sets During Production
			17.4.3.2 Removal of Unusable and Redundant Data Sets
				17.4.3.2.1 Generation of Training Data
	17.5	Results
		17.5.1	Modeling of Target Values
		17.5.2	Simulation and Optimization
	17.6	Monetary Consideration of Optimization
		17.6.1	Amortization Period Calculation
		17.6.2	Further Monetization Effects through Scrap Reduction
	17.7	Summary
	References
18 Added Value through Linking of Product and Process Data on the Example of a Textile Process Chain
	Abstract
	18.1	Classification of the Observed Monetization Effects
	18.2	Introduction
	18.3	Basics and Methods
		18.3.1	Aachen Textile Production Theory
		18.3.2	Digital Twin and Digital Shadow
		18.3.3	Data Warehousing
		18.3.4	Optimization with Regression Models
	18.4	Challenges and our Approach
		18.4.1	Challenges
		18.4.2	Solution Approaches
			18.4.2.1 Building a Machine Network
			18.4.2.2 Structure of the Data Warehouse
			18.4.2.3 Selection of Analysis Methods for Data Analysis
			18.4.2.4 Validation of our Method
			18.4.2.5 Concept for the Transfer of Results into Industry
	18.5	Results
	18.6	Discussion
	18.7	Summary
	References
19 Data-Based Knowledge Gain from the Perspective of Surface Technology
	Abstract
	19.1	Classification of the Observed Monetization Effects
	19.2	Introduction
	19.3	Fundamentals and Challenges
	19.4	Methods and Solutions
	19.5	Case Studies and Outlook
	19.6	Summary
	References
20 Digitization in the Plastics Processing
	Abstract
	20.1	Classification of the Observed Monetization Effects
	20.2	Introduction
	20.3	Monetization Through Digital Services
	20.4	Monetization Through Digital Services
	20.5	Monetization Through Improvement of Internal Processes
	20.6	Monetization of Anonymized and Generalized Production Data
	20.7	Conclusion
	References
21 Monetization of Data in Joining Technology
	Abstract
	21.1	Classification of the Observed Monetization Effects
	21.2	Introduction
	21.3	Basics and Methods
	21.4	Challenges and Solutions
		21.4.1	Use of Key Performance Indicators
		21.4.2	Settings for Welding
		21.4.3	Predictive Quality
	21.5	Discussion and Case Studies
		21.5.1	Cycle Time Optimization in Spot Welding
		21.5.2	Inverse Search of Parameters for given Weld Geometry in Gas Metal Arc Welding
		21.5.3	Process Stability Consideration during GMA Welding
		21.5.4	Systemic Optimization through Analysis and Interpretation of Welding Production Data
	21.6	Summary
	References
22 With Transparency to Zero-Defect Production and Added Values for the Customer
	Abstract
	22.1	Classification of the Observed Monetization Effects
	22.2	Introduction
	22.3	Background
	22.4	Solution Approaches
		22.4.1	Development of the Error
		22.4.2	Prevent known Defects
		22.4.3	Discovering Unknown
	22.5	Implementation
		22.5.1	Cost Reduction
	22.6	An Example from Practice
		22.6.1	Secure Implementation with IOTA Tangle
		22.6.2	Traceability
		22.6.3	Monetization
		22.6.4	Changes to Business Models
	22.7	Outlook
	22.8	Conclusion
	References
23 Decentralized Marketplace Structures as Protection against Information Asymmetries
	Abstract
	23.1	Introduction
	23.2	Basics
		23.2.1	Blockchain and DLT
		23.2.2	Internet of Things
	23.3	Challenges and Solutions
		23.3.1	Internet of Things
		23.3.2	Trade Secrets vs. Data Monetization
	23.4	Solution: A Decentralized IoT Marketplace
		23.4.1	Properties
		23.4.2	Who will be Affected by these Markets?
		23.4.3	Emerging Business Models and Processes
			23.4.3.1 Data-Driven Flexibility: New data Sources Effectively used to Expand Business Models
			23.4.3.2 Data Discovery as a Service
	23.5	Summary
	References
24 As-a-Service Models for Manufacturing Technology
	Abstract
	24.1	Classification of the Observed Monetization Effects
	24.2	Introduction
	24.3	Basics and Methods
		24.3.1	FE-Simulation of Fineblanking
		24.3.2	Data-driven Modeling of Fineblanking
			24.3.2.1 Modeling of Die Roll by Means of Artificial Neural Networks
			24.3.2.2 Modeling of the Die Roll by Means of Support Vector Machines
			24.3.2.3 Modeling of Die Roll with Statistical Regression Methods
			24.3.2.4 Validation of the Implemented Models
	24.4	Challenges and Solutions
	24.5	Cloud Computing Platform for the Digital Twin of Fineblanking as a Service
		24.5.1	Use Cases of the Platform
			24.5.1.1 Finite-Element Method as-a-Service
			24.5.1.2 Data-driven Modeling as-a-service
		24.5.2	Requirements Analysis
		24.5.3	Platform Architecture
	24.6	Summary
	References
25 Using Data from Agricultural and Construction Machinery profitably
	Abstract
	25.1	Classification of the Examined Monetization Effects
	25.2	Introduction
	25.3	Fundamentals
	25.4	Mechanisms and Obstacles to Data Monetization According to the State of the Art
		25.4.1	Mobile Machinery in the Construction Industry
			25.4.1.1 Data Value of Digital Solutions
			25.4.1.2 Interim Conclusion
		25.4.2	Mobile Machinery in Agriculture
			25.4.2.1 Data Value of Digital Solutions
			25.4.2.2 Interim Conclusion
		25.4.3	Conclusion on the Current Data Monetization in the Construction Industry and in Agriculture
	25.5	Solution Approach: Guideline for Developing Data-Driven Business Models
	25.6	Case Studies
		25.6.1	Construction Industry: Case Study Data-Driven Operator Model
		25.6.2	Construction Industry: Case Study Construction Project as a System
		25.6.3	Agriculture: Example Case of Trade in Product Information
	25.7	Summary
	References
26 Sustainable Production through Predictive Quality and Sustainability Analytics along the Supply Chain
	Abstract
	26.1	Classification of the Observed Monetization Effects
	26.2	Introduction
	26.3	Basics and Methods
		26.3.1	Predictive Quality and Sustainability Analytics
		26.3.2	Supplier and Customer Data
		26.3.3	Data Quality
	26.4	Monetization of Technical Data along the Supply Chain
	26.5	Examples of Applications
		26.5.1	Predictive Quality in the Automotive Industry
		26.5.2	Sustainability Analytics in the Production of Household Appliances
	26.6	Conclusion
	References
27 The Data Lifecycle from Data Capture to Insight
	Abstract
	27.1	Classification of the Observed Monetization Effects
	27.2	Perfect Processes in a Real World
	27.3	Generate and Provide Data
		27.3.1	From the Sensor into the Digital World
		27.3.2	Knowledge Creation through Consideration of Different Sources
		27.3.3	Expansion of the Data Basis through Artificial Data
	27.4	Enrich and Refine Data
		27.4.1	Raw Data
		27.4.2	Annotated Data
		27.4.3	Models and Information
		27.4.4	Enrichment with Knowledge
	27.5	Discussion and Outlook
		27.5.1	Technical Trends
			27.5.1.1 Synthetic Generation of Data
			27.5.1.2 Edge AI and Distributed AI
			27.5.1.3 Transfer Learning
			27.5.1.4 Quantum Machine Learning
		27.5.2	Legal Basis and Perspectives
	27.6	Summary
	References
28 “ReLIFE”: Business Models for Data-Based Remanufacturing
	Abstract
	28.1	Classification of the Observed Monetization Effects
	28.2	Introduction
	28.3	Basics of Maintenance
	28.4	Development of Data and Knowledge Sources
		28.4.1	The Capital Good as a Data Source
		28.4.2	Remanufacturing Measures as an Action Framework
	28.5	Adaptive Remanufacturing as an Individual Maintenance Strategy
	28.6	Business Models for Adaptive Remanufacturing
		28.6.1	Product-Service-System
		28.6.2	Monetization Scenarios for Technical Data from Capital Goods
		28.6.3	Validation of Business Models
	28.7	Summary
	References
29 Data Utilization and Data Reduction in Laser Material Processing
	Abstract
	29.1	Classification of the Observed Monetization Effects
	29.2	Introduction
	29.3	Basics and Methods
		29.3.1	Data Acquisition and Expectations
		29.3.2	Data Processing and Reduction
	29.4	Challenges and Solutions
	29.5	Discussion and Examples
		29.5.1	Sequence of Seams for Autonomous Laser Beam Welding
		29.5.2	Laser Beam Welding of Battery Cells
		29.5.3	Laser Beam Drilling of Filters for Water Treatment
	29.6	Summary
	References
30 Transparency and Value of Data in Construction
	Abstract
	30.1	Introduction
	30.2	Development and Current State
		30.2.1	Special Features of the Construction Industry and Construction Product
		30.2.2	Data Basis in Construction
	30.3	Exchange and Use of Building-Specific Data
		30.3.1	Challenges in Data Exchange
		30.3.2	Umbrella Format and Model Container as a Solution Approach
		30.3.3	Current Development and Results
	30.4	Data Evaluation in Construction
		30.4.1	Parametric Design and Algorithms
		30.4.2	Digital Models of Production in Construction
		30.4.3	Data-Driven Feasibility Analysis
	30.5	Summary
	References
31 Data Monetization Potential in the Automotive Industry Value Chain and the Product Lifecycle
	Abstract
	31.1	Classification of the Observed Monetization Effects
	31.2	Introduction
	31.3	Basics and Methods
		31.3.1	Value Chain Within the Automotive Industry
		31.3.2	Product Lifecycle Management (PLM)
		31.3.3	Industry 4.0, Data Analysis Methodology and Artificial Intelligence
	31.4	Challenges and Solutions
	31.5	Applications
		31.5.1	Vehicle Value Chain
		31.5.2	Life Cycle Management
		31.5.3	Employee Motivation
	31.6	Discussion and Case Studies
		31.6.1	Product Lifecycle Management Tool
		31.6.2	IoT Examples in Production
		31.6.3	Logistics
	31.7	Summary
	References
Part V Data Monetization in Energy Technology
32 Technology Convergence in the Energy Sector
	Abstract
	32.1	Introduction
	32.2	Progressive Application System
		32.2.1	Technologies
		32.2.2	Functions
		32.2.3	Features and Potential
		32.2.4	State of the Art and Market Adoption
	32.3	Progressive Application System in the Energy Sector
		32.3.1	Remote Read out of Consumption Metering Systems
		32.3.2	Monetization Effects
	32.4	Summary
	References
33 Data Monetization in the Energy System and its Role in the Development of a Customer-Oriented Power Grid
	Abstract
	33.1	Classification of the Observed Monetization Effects
	33.2	Introduction
		33.2.1	Data Exchange Between Actors
	33.3	The End Customer as an Active Participant in the Energy Market Two Possible Applications
		33.3.1	Flexibility Markets
		33.3.2	Local Energy Communities
	33.4	Conclusion
	References
Part VI Data Monetization in other emerging application fields
34 Monetization of Sensor Data in Commercial Real Estate
	Abstract
	34.1	Classification of the Observed Monetization Effects
	34.2	Introduction
	34.3	Basics
	34.4	Monetization
		34.4.1	Internal Data Monetization in Commercial Real Estate
		34.4.2	External Data Monetization in Commercial Real Estate
	34.5	Challenges and Solutions
		34.5.1	Challenges
		34.5.2	Solution Approach
	34.6	Conclusion
	References
35 Saving Costly Experiments and Simulations through Machine Learning
	Abstract
	35.1	Classification of the Observed Monetization Effects
	35.2	Introduction
	35.3	Fundamentals and Methods
		35.3.1	Simplified Residual Neural Network (SimResNet)
		35.3.2	Ensemble Kalman Filter (EnKF)
	35.4	Challenges and Solutions
	35.5	Applications and Results
		35.5.1	Determination of Cutting Forces in Machining of Metallic Materials
		35.5.2	Atmospheric Plasma Spraying
	35.6	Discussion
	35.7	Summary
	References
36 PigConomy
	Abstract
	36.1	Classification of the Observed Monetization Effects
	36.2	Introduction
	36.3	Electronic Marketplaces
	36.4	PigConomy—Generating and Marketing Knowledge in Animal Husbandry
		36.4.1	Functional Components in Detail—Data as Raw Material of the PigConomy
		36.4.2	Functional Components in Detail—Statistical Analysis as a Tool of PigConomy
		36.4.3	Functional Components in Detail—Blockchain as a Clearing House for PigConomy
	36.5	Core Results of Our Proof of Concept
	36.6	From Proof of Concept to PigConomy Ecosystem
	36.7	Findings for the Monetization of Data
	36.8	Summary
	References