Industry 4.0 is becoming more and more important for manufacturers as the developments in the area of Internet of Things advance. Another technology gaining more attention is data stream processing systems. Although such streaming frameworks seem to be a natural fit for Industry 4.0 scenarios, their application in this context is still low. The contributions in this paper are threefold. Firstly, we present industry findings that we derived from site inspections with a focus on Industry 4.0. Moreover, our view on Industry 4.0 and important related aspects is elaborated. As a third contribution, we illustrate our opinion on why data stream processing technologies could act as an enabler for Industry 4.0 and point out possible obstacles on this way.
With respect to Industry 4.0, there are many existing definitions and views published. An overview of selected perceptions of Industry 4.0 is presented in @cite_7 . Moreover, it also states that there is no generally accepted definition for the term Industry 4.0.
@cite_7
2530328109
The increasing integration of the Internet of Everything into the industrial value chain has built the foundation for the next industrial revolution called Industrie 4.0. Although Industrie 4.0 is currently a top priority for many companies, research centers, and universities, a generally accepted understanding of the term does not exist. As a result, discussing the topic on an academic level is difficult, and so is implementing Industrie 4.0 scenarios. Based on a quantitative text analysis and a qualitative literature review, the paper identifies design principles of Industrie 4.0. Taking into account these principles, academics may be enabled to further investigate on the topic, while practitioners may find assistance in identifying appropriate scenarios. A case study illustrates how the identified design principles support practitioners in identifying Industrie 4.0 scenarios. Abstract Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5-level architecture is proposed as a guideline for implementation of CPS. Facing a wide range of new technologies and best practices within Industry 4.0, companies are seeking a systematic approach to identify potential application scenarios in their production. Best practices are often customized use cases – generally too specific to apply in a different manufacturing setting right away. The Production Assessment 4.0 presents a pragmatic approach to support companies to develop Industry 4.0 use cases in their factories. The approach follows the Design Thinking method and focus especially on the human role as a key aspect in the use cases designing process. 0 Introduction.- I Themes and Problems.- 1.0 Introduction.- 1.1 The theory of finance capital.- 1.2 Interlocking directorates and economic power.- 1.3 Financial groups.- 1.4 Corporate elite and capitalist class.- 1.5 Summary.- II Between Market and Hierarchy.- 2.0 Introduction.- 2.1 The organization of firms and markets: a neo-classical explanation.- 2.2 Some definitions of firms.- 2.3 Power and control.- 2.4 Ownership and control.- 2.5 Competition, cooperation and control.- 2.5.1 Concentration and centralization.- 2.5.2 The level-of-analysis problem.- 2.6 Summary.- III Imperialism in the Seventies: two models.- 3.0 Introduction.- 3.1 Theories of imperialism.- 3.1.1 Their origins.- 3.1.2 After World War II.- 3.2 Two models.- 3.3 Research design.- 3.4 Summary.- IV The International Corporate Elite.- 4.0 Introduction.- 4.1 The organization of the supervising and executive function in different countries.- 4.2 Selection of the international corporate elite.- 4.3 Network characteristics of the international corporate elite.- 4.3.0 Introduction.- 4.3.1 The finance capitalists.- 4.3.2 The big linkers.- 4.4 Types of interlocking directorates.- 4.4.0 Introduction.- 4.4.1 Multiple interlocks.- 4.5 Summary.- V National Versus International Integration.- 5.0 Introduction to some graph-theoretical concepts.- 5.1 General patterns in the international network.- 5.1.1 Compactness of the international network.- 5.1.2 International integration of national networks.- 5.2 The international network of Western firms.- 5.2.0 Introduction.- 5.2.1 Local centrality.- 5.2.2 Global centrality.- 5.2.3 Overall centrality.- 5.3 Industrial concentration versus economic centralization.- 5.4 Summary.- VI Domination and Control.- 6.0 Introduction.- 6.1 Clusters of heavily interlocked firms.- 6.1.1 The network at multiplicity-level two.- 6.1.2 The network at multiplicity-level three.- 6.1.3 Conclusions.- 6.2 The network of officer-interlocks.- 6.2.0 Introduction.- 6.2.1 Domination and control.- 6.2.1.1 The network of all officer-interlocks.- 6.2.1.2 The network of control.- 6.2.2 Financial groups.- 6.2.3 Constellations of interests.- 6.3 Summary.- VII Competition and Cooperation: the role of banks.- 7.0 Introduction.- 7.1 Interlocks among banks.- 7.2 Overlapping spheres of interests.- 7.3 International bank consortia.- 7.4 The American banks.- 7.5 Summary.- VIII The Impact of World Crisis: changes in the network.- 8.0 Introduction.- 8.1 A new economic world order?.- 8.2 Selection of the 1976 sample.- 8.3 The international corporate elite.- 8.3.1 Finance capitalists.- 8.3.2 Big linkers.- 8.4 International versus national integration.- 8.5 Centrality in the nested networks (1976).- 8.6 Domination and control in the 1976 network.- 8.7 Summary.- IX Summary and Conclusions.- 9.1 Introduction.- 9.2 The structure of the international corporate elite.- 9.3 Conflict or cooperation.- 9.4 The meaning of interlocking directorates.- 9.5 The international corporate elite.- References.- Authors Index.- Firms Index.- Appendix A. Abstract Lean Production is widely recognized and accepted in the industrial setting. It concerns the strict integration of humans in the manufacturing process, a continuous improvement and focus on value-adding activities by avoiding waste. However, a new paradigm called Industry 4.0 or the fourth industrial revolution has recently emerged in the manufacturing sector. It allows creating a smart network of machines, products, components, properties, individuals and ICT systems in the entire value chain to have an intelligent factory. So, now a question arises if, and how these two approaches can coexist and support each other. Under the context of Industrie 4.0 (I4.0), future production systems provide balanced operations between manufacturing flexibility and efficiency, realized in an autonomous, horizontal, and decentralized item-level production control framework. Structured interoperability via precise formulations on an appropriate degree is crucial to achieve software engineering efficiency in the system life cycle. However, selecting the degree of formalization can be challenging, as it crucially depends on the desired common understanding (semantic degree) between multiple parties. In this paper, we categorize different semantic degrees and map a set of technologies in industrial automation to their associated degrees. Furthermore, we created guidelines to assist engineers selecting appropriate semantic degrees in their design. We applied these guidelines on publicly available scenarios to examine the validity of the approach, and identified semantic elements over internally developed use cases concerning plug-and-produce. Under the context of Industrie 4.0 (I4.0), future production systems provide balanced operations between manufacturing flexibility and efficiency, realized in an autonomous, horizontal, and decentralized item-level production control framework. Structured interoperability via precise formulations on an appropriate degree is crucial to achieve engineering efficiency in the system life cycle. However, selecting the degree of formalization can be challenging, as it crucially depends on the desired common understanding (semantic degree) between multiple parties. In this paper, we categorize different semantic degrees and map a set of technologies in industrial automation to their associated degrees. Furthermore, we created guidelines to assist engineers selecting appropriate semantic degrees in their design. We applied these guidelines on publically available scenarios to examine the validity of the approach, and identified semantic elements over internally developed use cases targeting semantically-enabled plug-and-produce. Recent advances in ICT enable the evolution of the manufacturing industry to meet the new requirements of the society. Cyber-physical systems, Internet-of-Things (IoT), and Cloud computing, play a key role in the fourth industrial revolution known as Industry 4.0. The microservice architecture has evolved as an alternative to SOA and promises to address many of the challenges in software development. In this paper, we adopt the concept of microservice and describe a framework for manufacturing systems that has the cyber-physical microservice as the key construct. The manufacturing plant processes are defined as compositions of primitive cyber-physical microservices adopting either the orchestration or the choreography pattern. IoT technologies are used for system integration and model-driven engineering is utilized to semi-automate the development process for the industrial engineer, who is not familiar with microservices and IoT. Two case studies demonstrate the feasibility of the proposed approach. Modeling engineering knowledge explicitly and representing it by means of standardized modeling languages and in machine-understandable form enables advanced engineering processes in industrial and factory automation. This affects positively both process and product quality. In this paper we explore how the AutomationML format, an emerging data exchange standard, that supports the Industry 4.0 vision, can be represented by means of two established modeling approaches - Model-Driven Engineering (MDE) and Semantic Web. We report observed differences w.r.t. resulting model features and model creation process and, additionally, present the application possibilities of the developed models for engineering process improvement in a production system engineering context. iv 1 Problem Definition 1 2 Origins from Two Extremes 3 2.1 The Origins of Agile Methods 3 2.2 The Origins of CMMI 5 3 Factors that Affect Perception 7 3.1 Misuse 7 3.2 Lack of Accurate Information 8 3.3 Terminology Difficulties 9 4 The Truth About CMMI 11 4.1 CMMI Is a Model, Not a Process Standard 11 4.2 Process Areas, Not Processes 13 4.3 SCAMPI Appraisals 14 5 The Truth About Agile 16 6 There Is Value in Both Paradigms 20 6.1 Challenges When Using Agile 20 6.2 Challenges When Using CMMI 22 6.3 Current Published Research 23 6.4 The Hope in Recent Trends 24 7 Problems Not Solved by CMMI nor Agile 27 In conjunction with what is often called the industry 4.0, the new machine age, or the rise of the robots, the authors of this paper have each experienced the following phenomenon. At public events and roundtable discussions, among our circles of friends, or during interviews with the media, we are asked on a surprisingly regular basis: "How must humankind adapt to the imminent process of technological change? What do we have to learn in order to keep pace with the smart new machines? What new skills do we need to understand the robots?" Cyber-physical systems and the Internet-of-Things are getting more and more traction in different application areas. Boosted by initiatives such as Industrie 4.0 in Germany or the Industrial Internet Consortium in the US, they are enablers for innovation in industrial automation. To provide the advanced flexibility in production envisioned for future automation systems, Programmable Logic Controllers (PLCs), as one of their main building blocks, also need to become more flexible. However, the conservative nature of this domain prohibits changes in the controller architecture impacting the installed base. Currently there exist various approaches that evolve control architectures to the next level, but none of them address flexible function deployment at the same time with legacy support. In this paper, we present a an architecture for a multi-purpose controller that is inspired by the virtualization trend in cloud systems which moves from heavyweight virtual machines to lightweight containers solutions such as LXC or Docker. Our solution includes the support for multiple PLC execution engines and adds support for the emulation of legacy engines as well. We evaluate this architecture by executing performance measurements that analyze the impact of container technologies to the real-time aspects of PLC engines. Maintaining critical data access latency requirements and ensuring efficient energy consumption on the field devices are two important challenges of Industry 4.0. The traditional, centralized industrial networks which offer primitive data distribution functions, might be incapable of meeting such strict requirements. In this paper, in order to overcome this issue, we focus on a network of resource constraint IoT devices, and exploit the presence of a few more capable Edge nodes that act as distributed local data storing proxies for all IoT devices. We show that, given the proxy locations in the network, the initial energy supplies of the nodes, a pattern of data requests from IoT devices, and the maximum access latency that consumer nodes can tolerate, the problem of finding how to distribute the data on the Edge nodes maximizing the network lifetime is computationally hard. We design an offline centralized heuristic algorithm for identifying which paths in the network the data should follow and on which proxies they should be cached, in order to meet the data access latency constraint and to maximize the network lifetime. We implement the method and evaluate its performance using a testbed of IEEE 802.15.4-enabled network nodes. We demonstrate that the proposed heuristic (i) guarantees data access latency below the given threshold, and (ii) performs well in terms of network lifetime with respect to a theoretically optimal solution. Abstract Cyber-physical systems and the Internet-of-Things are getting more and more traction in different application areas. Boosted by initiatives such as Industrie 4.0 in Germany or the Industrial Internet Consortium in the US, they are enablers for innovation in industrial automation. To provide the advanced flexibility in production envisioned for future automation systems, Programmable Logic Controllers (PLCs), as one of their main building blocks, also need to become more flexible. However, the conservative nature of this domain prohibits changes in the controller architecture impacting the installed base. Currently there exist various approaches that evolve control architectures to the next level, but none of them address flexible function deployment at the same time with legacy support. In this paper, we present an architecture for a multi-purpose controller that is inspired by the virtualization trend in cloud systems which moves from heavyweight virtual machines to lightweight containers solutions such as LXC or Docker. Our solution includes the support for multiple PLC execution engines and adds support for the emulation of legacy engines as well. We evaluate this architecture by executing performance measurements that analyze the impact of container technologies to the real-time aspects of PLC engines. Several networking architectures have been developed atop IEEE 802.3 networks to provide real-time communication guarantees for time-sensitive applications in industrial automation systems. The basic principle underlying these technologies is the precise transmission scheduling of time-triggered traffic through the network for providing deterministic and bounded latency and jitter. These transmission schedules are typically synthesized offline (computational time in the order of hours) and remain fixed thereafter, making it difficult to dynamically add or remove network applications. This paper presents algorithms for incrementally adding time-triggered flows in a time-sensitive software-defined network (TSSDN). The TSSDN is a network architecture based on software-defined networking, which provides real-time guarantees for time-triggered flows by scheduling their transmissions on the hosts (network edge) only. These algorithms exploit the global view of the control plane on the data plane to schedule and route time-triggered flows needed for the dynamic applications in the Industrial Internet of Things (Industry 4.0). The evaluations show that these algorithms can compute incremental schedules for time-triggered flows in subseconds with an average relative optimality of 68 . The Industry 4.0 is a vision that includes connecting more intensively physical systems with their virtual counterparts in computers. This computerization of manufacturing will bring many advantages, including allowing data gathering, integration and analysis in the scale not seen earlier. In this paper we describe our Semantic Big Data Historian that is intended to handle large volumes of heterogeneous data gathered from distributed data sources. We describe the approach and implementation with a special focus on using Semantic Web technologies for integrating the data. Three studies were conducted comparing speed of performance, error rates and user preference ratings for three selection devices. The devices tested were a touchscreen, a touchscreen with stabilization (stabilization software filters and smooths raw data from hardware), and a mouse. The task was the selection of rectangular targets 1, 4, 16 and 32 pixels per side (0·4 × 0·6, 1·7 × 2·2, 6·9 × 9·0, 13·8 × 17·9 mm respectively). Touchscreen users were able to point at single pixel targets, thereby countering widespread expectations of poor touchscreen resolution. The results show no difference in performance between the mouse and touchscreen for targets ranging from 32 to 4 pixels per side. In addition, stabilization significantly reduced the error rates for the touchscreen when selecting small targets. These results imply that touchscreens, when properly used, have attractive advantages in selecting targets as small as 4 pixels per size (approximately one-quarter of the size of a single character). A variant of Fitts' Law is proposed to predict touchscreen pointing times. Ideas for future research are also presented. 2 Static Algebras and Updates 4 2.1 Static Algebras: Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Vocabularies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Definition of Static Algebras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.4 Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.5 Locations and Updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.6 Update Sets and Families of Update Sets . . . . . . . . . . . . . . . . . . . . . . . . 6 2.7 Conservative Determinism vs. Local Nondeterminism . . . . . . . . . . . . . . . . . . 7 1. Introduction.- 1.1 Design Styles for VLSI Systems.- 1.2 Automatic Logic Synthesis.- 1.3 PLA Implementation.- 1.4 History of Logic Minimization.- 1.5 ESPRESSO-II.- 1.6 Organization of the Book.- 2. Basic Definitions.- 2.1 Operations on Logic Functions.- 2.2 Algebraic Representation of a Logic Function.- 2.3 Cubes and Covers.- 3. Decomposition and Unate Functions.- 3.1 Cofactors and the Shannon Expansion.- 3.2 Merging.- 3.3 Unate Functions.- 3.4 The Choice of the Splitting Variable.- 3.5 Unate Complementation.- 3.6 SIMPLIFY.- 4. The ESPRESSO Minimization Loop and Algorithms.- 4.0 Introduction.- 4.1 Complementation.- 4.2 Tautology.- 4.2.1 Vanilla Recursive Tautology.- 4.2.2 Efficiency Results for Tautology.- 4.2.3 Improving the Efficiency of Tautology.- 4.2.4 Tautology for Multiple-Output Functions.- 4.3 Expand.- 4.3.1 The Blocking Matrix.- 4.3.2 The Covering Matrix.- 4.3.3 Multiple-Output Functions.- 4.3.4 Reduction of the Blocking and Covering Matrices.- 4.3.5 The Raising Set and Maximal Feasible Covering Set.- 4.3.6 The Endgame.- 4.3.7 The Primality of c+.- 4.4 Essential Primes.- 4.5 Irredundant Cover.- 4.6 Reduction.- 4.6.1 The Unate Recursive Paradigm for Reduction.- 4.6.2 Establishing the Recursive Paradigm.- 4.6.3 The Unate Case.- 4.7 Lastgasp.- 4.8 Makesparse.- 4.9 Output Splitting.- 5. Multiple-Valued Minimization.- 6. Experimental Results.- 6.1 Analysis of Raw Data for ESPRESSO-IIAPL.- 6.2 Analysis of Algorithms.- 6.3 Optimality of ESPRESSO-II Results.- 7. Comparisons and Conclusions.- 7.1 Qualitative Evaluation of Algorithms of ESPRESSO-II.- 7.2 Comparison with ESPRESSO-IIC.- 7.3 Comparison of ESPRESSO-II with Other Programs.- 7.4 Other Applications of Logic Minimization.- 7.5 Directions for Future Research.- References. 1 Preliminaries.- 1.1 Introduction.- 1.2 Measures and Functions.- 1.3 Weak Topologies.- 1.4 Convergence of Measures.- 1.5 Complements.- 1.6 Notes.- I Markov Chains and Ergodicity.- 2 Markov Chains and Ergodic Theorems.- 2.1 Introduction.- 2.2 Basic Notation and Definitions.- 2.3 Ergodic Theorems.- 2.4 The Ergodicity Property.- 2.5 Pathwise Results.- 2.6 Notes.- 3 Countable Markov Chains.- 3.1 Introduction.- 3.2 Classification of States and Class Properties.- 3.3 Limit Theorems.- 3.4 Notes.- 4 Harris Markov Chains.- 4.1 Introduction.- 4.2 Basic Definitions and Properties.- 4.3 Characterization of Harris recurrence.- 4.4 Sufficient Conditions for P.H.R.- 4.5 Harris and Doeblin Decompositions.- 4.6 Notes.- 5 Markov Chains in Metric Spaces.- 5.1 Introduction.- 5.2 The Limit in Ergodic Theorems.- 5.3 Yosida's Ergodic Decomposition.- 5.4 Pathwise Results.- 5.5 Proofs.- 5.6 Notes.- 6 Classification of Markov Chains via Occupation Measures.- 6.1 Introduction.- 6.2 A Classification.- 6.3 On the Birkhoff Individual Ergodic Theorem.- 6.4 Notes.- II Further Ergodicity Properties.- 7 Feller Markov Chains.- 7.1 Introduction.- 7.2 Weak-and Strong-Feller Markov Chains.- 7.3 Quasi Feller Chains.- 7.4 Notes.- 8 The Poisson Equation.- 8.1 Introduction.- 8.2 The Poisson Equation.- 8.3 Canonical Pairs.- 8.4 The Cesaro-Averages Approach.- 8.5 The Abelian Approach.- 8.6 Notes.- 9 Strong and Uniform Ergodicity.- 9.1 Introduction.- 9.2 Strong and Uniform Ergodicity.- 9.3 Weak and Weak Uniform Ergodicity.- 9.4 Notes.- III Existence and Approximation of Invariant Probability Measures.- 10 Existence of Invariant Probability Measures.- 10.1 Introduction and Statement of the Problems.- 10.2 Notation and Definitions.- 10.3 Existence Results.- 10.4 Markov Chains in Locally Compact Separable Metric Spaces.- 10.5 Other Existence Results in Locally Compact Separable Metric Spaces.- 10.6 Technical Preliminaries.- 10.7 Proofs.- 10.8 Notes.- 11 Existence and Uniqueness of Fixed Points for Markov Operators.- 11.1 Introduction and Statement of the Problems.- 11.2 Notation and Definitions.- 11.3 Existence Results.- 11.4 Proofs.- 11.5 Notes.- 12 Approximation Procedures for Invariant Probability Measures.- 12.1 Introduction.- 12.2 Statement of the Problem and Preliminaries.- 12.3 An Approximation Scheme.- 12.4 A Moment Approach for a Special Class of Markov Chains.- 12.5 Notes.