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AI technologies such as deep learning provide promising advances in many areas. Using these technologies, enterprises and organizations implement new business models and capabilities. In the beginning, AI-technologies have been deployed in an experimental environment. AI-based applications have been created in an ad-hoc manner and without methodological guidance or engineering approach. Due to the increasing importance of AI-technologies, however, a more structured approach is necessary that enable the methodological engineering of AI-based applications. Therefore, we develop in this paper first steps towards methodological engineering of AI-based applications. First, we identify some important differences between the technological foundations of AI- technologies, in particular deep learning, and traditional information technologies. Then we create a framework that enables to engineer AI-applications using four steps: identification of an AI-application type, sub-type identification, lifecycle phase, and definition of details. The introduced framework considers that AI-applications use an inductive approach to infer knowledge from huge collections and streams of data. It not only enables the rapid development of AI-application but also the efficient sharing of knowledge on AI-applications.
Today, many companies are adapting their strategy, business models, products, services as well as business processes and information systems in order to expand their digitalization level through intelligent systems and services. The paper raises an important question: What are cognitive co-creation mechanisms for extending digital services and architectures to readjust the usage value of smart services? Typically, extensions of digital services and products and their architectures are manual design tasks that are complex and require specialized, rare experts. The current publication explores the basic idea of extending specific digital artifacts, such as intelligent service architectures, through mechanisms of cognitive co-creation to enable a rapid evolutionary path and better integration of humans and intelligent systems. We explore the development of intelligent service architectures through a combined, iterative, and permanent task of co-creation between humans and intelligent systems as part of a new concept of cognitively adapted smart services. In this paper, we present components of a new platform for the joint co-creation of cognitive services for an ecosystem of intelligent services that enables the adaptation of digital services and architectures.
Our paper gives first answers on a fundamental question: how can the design of architectures of intelligent digital systems and services be accomplished methodologically? Intelligent systems and services are the goals of many current digitalization efforts today and part of massive digital transformation efforts based on digital technologies. Digital systems and services are the foundation of digital platforms and ecosystems. Digtalization disrupts existing businesses, technologies, and economies and promotes the architecture of open environments. This has a strong impact on new value-added opportunities and the development of intelligent digital systems and services. Digital technologies such as artificial intelligence, the Internet of Things, services computing, cloud computing, big data with analytics, mobile systems, and social enterprise networks systems are important enablers of digitalization. The current publication presents our research on the architecture of intelligent digital ecosystems and products and services influenced by the service-dominant logic. We present original methodological extensions and a new reference model for digital architectures with an integral service and value perspective to model intelligent systems and services that effectively align digital strategies and architectures with artificial intelligence as main elements to support intelligent digitalization.
Power line communications (PLC) reuse the existing power-grid infrastructure for the transmission of data signals. As power line the communication technology does not require a dedicated network setup, it can be used to connect a multitude of sensors and Internet of Things (IoT) devices. Those IoT devices could be deployed in homes, streets, or industrial environments for sensing and to control related applications. The key challenge faced by future IoT-oriented narrowband PLC networks is to provide a high quality of service (QoS). In fact, the power line channel has been traditionally considered too hostile. Combined with the fact that spectrum is a scarce resource and interference from other users, this requirement calls for means to increase spectral efficiency radically and to improve link reliability. However, the research activities carried out in the last decade have shown that it is a suitable technology for a large number of applications. Motivated by the relevant impact of PLC on IoT, this paper proposed a cooperative spectrum allocation in IoT-oriented narrowband PLC networks using an iterative water-filling algorithm.
Automatic classification of rotating machinery defects using Machine Learning (ML) algorithms
(2020)
Electric machines and motors have been the subject of enormous development. New concepts in design and control allow expanding their applications in different fields. The vast amount of data have been collected almost in any domain of interest. They can be static; that is to say, they represent real-world processes at a fixed point of time. Vibration analysis and vibration monitoring, including how to detect and monitor anomalies in vibration data are widely used techniques for predictive maintenance in high-speed rotating machines. However, accurately identifying the presence of a bearing fault can be challenging in practice, especially when the failure is still at its incipient stage, and the signal-to-noise ratio of the monitored signal is small. The main objective of this work is to design a system that will analyze the vibration signals of a rotating machine, based on recorded data from sensors, in the time/frequency domain. As a consequence of such substantial interest, there has been a dramatic increase of interest in applying Machine Learning (ML) algorithms to this task. An ML system will be used to classify and detect abnormal behavior and recognize the different levels of machine operation modes. The proposed solution can be deployed as predictive maintenance for Industry 4.0.
Business process models provide a considerable number of benefits for enterprises and organizations, but the creation of such models is costly and time-consuming, which slows down the organizational adoption of business process modeling. Social paradigms pave new ways for business process modeling by integrating stakeholders and leveraging knowledge sources. However, empirical research about the impact of social paradigms on costs of business process modeling is sparse. A better understanding of their impact could help to reduce the cost of business process modeling and improve decision-making on BPM activities. The paper constributes to this field by reporting about an empirical investigation via survey research on the perceived influence of different cost factors among experts. Our results indicate that different cost components, as well as the use of social paradigms, influence cost.
Internet of Things (IoT) provides a strong platform for computer users to connect objects, devices, and people to the Internet for exchanging or sharing of information with each other. IoT is growing rapidly and is expected to adapt to disciplines such as manufacturing, agriculture, healthcare, and robotics. Furthermore, the new concept of IoT is proposed and shown, especially for robotics areas as Internet of Robotics Things (IoRT). IoRT is a mixed structure of diverse technologies such as cloud computing, artificial intelligence, and machine learning. However, to promote and realize IoRT, digitization and digital transformation should be proceeded and implemented in the robotics enterprise. In this paper, we propose and architecture framework for IoRT-based digital platforms an verify it using a planned case in a global robotics enterprise. The associated challenges and future research directions in this field are also presented.
Due to the consequential impact of technological breakdowns, companies have to be prepared to deal with breakdowns or even better prevent them. In today's information technology, several methods and tools exist to downscale this concern. Therefore, this paper deals with the initial determination of a resilient enterprise architecture supporting predictive maintenance in the information technology domain and furthermore, concerns several mechanisms on how to reactively and proactively secure the state of resiliency on several abstraction levels. The objective of this paper is to give an overview on existing mechanisms for resiliency and to describe the foundation of an optimized approach, combining infrastructure and process mining techniques.
Enterprise architecture (EA) is useful for promoting digital transformation in global companies and information societies. In this paper, the authors investigated and analyzed the process for digital transformation in global companies, together with related work in using and applying an enterprise architecture framework for the digital era named the adaptive integrated digital architecture framework (AIDAF). Moreover, they position the AIDAF framework for processing digital transformation in global companies. Based on this analysis, the authors propose and describe a new enterprise architecture process for promoting digital transformation in global companies. Furthermore, the authors propose an adaptive EA cycle-based architecture board framework on digital platforms, while verifying them with case studies in global companies. Finally, the authors clarify the challenges and critical success factors of the process and framework for digital transformation with architecture board reviews in the adaptive EA cycle to assist EA practitioners with its implementation.
Enterprise architecture (EA) is useful for effectively structuring digital platforms with digital transformation in information societies. Moreover, digital platforms in the healthcare industry accelerate and increase the efficiency of drug discovery and development processes. However, there is the lack of knowledge concerning relationships between EA and digital platforms, in spite of the needs of it. In this paper, we investigated and analyzed the process of drug design and development within the healthcare industry, together with related work in using an enterprise architecture framework for the digital era named the Adaptive Integrated Digital Architecture Framework (AIDAF), specifically supporting the design of digital platforms there. Based on this analysis, we evaluate a method and propose a new reference architecture for promoting digital platforms in the healthcare industry, with future specific aspects of them making effective use of Artificial Intelligence (AI). The practical and theoretical contributions include: (1) Streamlined processes through digital platforms in organizations. (2) Informal knowledge supply and sharing among organizational members through digital platforms. (3) Efficiency and effectiveness in planning production and business for drug development. The findings indicate that EA with digital platforms using the AIDAF contribute to digital transformation with effectiveness for new drugs in the healthcare industry.