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Digitalization and enterprise architecture management: a perspective on benefits and challenges
(2023)
Many companies digitally transform their business models, processes, and services. They have also been using Enterprise Architecture Management approaches for a long time to synchronize corporate strategy and information technology. Such digitalization projects bring different challenges for Enterprise Architecture Management. Without understanding and addressing them, Enterprise Architecture Management projects will fail or not deliver the expected value. Since existing research has not yet addressed these challenges, they were investigated based on a qualitative expert study with leading industry experts from Europe. Furthermore, potential benefits of digitalization projects for Enterprise Architecture Management were researched. Our results provide a theoretical framework consisting of five identified challenges, triggers and a number of benefits. Furthermore, we discuss in what ways digitalization and EAM is a promising topic for future research.
Assistant platforms
(2023)
Many assistant systems have evolved toward assistant platforms. These platforms combine a range of resources from various actors via a declarative and generative interface. Among the examples are voice-oriented assistant platforms like Alexa and Siri, as well as text-oriented assistant platforms like ChatGPT and Bard. They have emerged as valuable tools for handling tasks without requiring deeper domain expertise and have received large attention with the present advances in generative artificial intelligence. In view of their growing popularity, this Fundamental outlines the key characteristics and capabilities that define assistant platforms. The former comprise a multi-platform architecture, a declarative interface, and a multi-platform ecosystem, while the latter include capabilities for composition, integration, prediction, and generativity. Based on this framework, a research agenda is proposed along the capabilities and affordances for assistant platforms.
The euphoria around microservices has decreased over the years, but the trend of modernizing legacy systems to this novel architectural style is unbroken to date. A variety of approaches have been proposed in academia and industry, aiming to structure and automate the often long-lasting and cost-intensive migration journey. However, our research shows that there is still a need for more systematic guidance. While grey literature is dominant for knowledge exchange among practitioners, academia has contributed a significant body of knowledge as well, catching up on its initial neglect. A vast number of studies on the topic yielded novel techniques, often backed by industry evaluations. However, practitioners hardly leverage these resources. In this paper, we report on our efforts to design an architecture-centric methodology for migrating to microservices. As its main contribution, a framework provides guidance for architects during the three phases of a migration. We refer to methods, techniques, and approaches based on a variety of scientific studies that have not been made available in a similarly comprehensible manner before. Through an accompanying tool to be developed, architects will be in a position to systematically plan their migration, make better informed decisions, and use the most appropriate techniques and tools to transition their systems to microservices.
Handling complexity in modern software engineering : editorial introduction to issue 32 of CSIMQ
(2022)
The potential of the Internet and related digital technologies, such as the Internet of Things (IoT), cognition and artificial intelligence, data analytics, services computing, cloud computing, mobile systems, collaboration networks, and cyber-physical systems, are both strategic drivers and enablers of modern digital platforms with fast-evolving ecosystems of intelligent services for digital products. This issue of CSIMQ presents three recent articles on modern software engineering. First, we focus on continuous software development and place it in the context of software architectures and digital transformation. The first contribution is followed by the description of the basis of specific security requirements and adequate digital monitoring mechanisms. Finally, we present a practical example of the digital management of livestock farming.
Theory and practice of implementing a successful enterprise IoT strategy in the industry 4.0 era
(2021)
Since the arrival of the internet and affordable access to technologies, digital technologies have occupied a growing place in industries, propelling us towards a 4th industrial revolution: Industry 4.0. In today’s era of digital upheaval, enterprises are increasingly undergoing transformations that are leading to their digitalization. The traditional manufacturing industry is in the throes of a digital transformation that is accelerated by exponentially growing technologies (e.g., intelligent robots, Internet of Things, sensors, 3D printing). Around the world, enterprises are in a frantic race to implement solutions based on IoT to improve their productivity, innovation, and reduce costs and improve their markets on the international scene. Considering the immense transformative potential that IoTs and big data have to bring to the industrial sector, the adoption of IoT in all industrial systems is a challenge to remain competitive and thus transform the industry into a smart factory. This paper presents the description of the innovation and digitalization process, following the Industry 4.0 paradigm to implement a successful enterprise IoT strategy.
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.
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.
Context
Microservices as a lightweight and decentralized architectural style with fine-grained services promise several beneficial characteristics for sustainable long-term software evolution. Success stories from early adopters like Netflix, Amazon, or Spotify have demonstrated that it is possible to achieve a high degree of flexibility and evolvability with these systems. However, the described advantageous characteristics offer no concrete guidance and little is known about evolvability assurance processes for microservices in industry as well as challenges in this area. Insights into the current state of practice are a very important prerequisite for relevant research in this field.
Objective
We therefore wanted to explore how practitioners structure the evolvability assurance processes for microservices, what tools, metrics, and patterns they use, and what challenges they perceive for the evolvability of their systems.
Method
We first conducted 17 semi-structured interviews and discussed 14 different microservice-based systems and their assurance processes with software professionals from 10 companies. Afterwards, we performed a systematic grey literature review (GLR) and used the created interview coding system to analyze 295 practitioner online resources.
Results
The combined analysis revealed the importance of finding a sensible balance between decentralization and standardization. Guidelines like architectural principles were seen as valuable to ensure a base consistency for evolvability and specialized test automation was a prevalent theme. Source code quality was the primary target for the usage of tools and metrics for our interview participants, while testing tools and productivity metrics were the focus of our GLR resources. In both studies, practitioners did not mention architectural or service-oriented tools and metrics, even though the most crucial challenges like Service Cutting or Microservices Integration were of an architectural nature.
Conclusions
Practitioners relied on guidelines, standardization, or patterns like Event-Driven Messaging to partially address some reported evolvability challenges. However, specialized techniques, tools, and metrics are needed to support industry with the continuous evaluation of service granularity and dependencies. Future microservices research in the areas of maintenance, evolution, and technical debt should take our findings and the reported industry sentiments into account.
Automatic anode rod inspection in aluminum smelters using deep-learning techniques: a case study
(2020)
Automatic fault detection using machine learning has become an exciting and promising area of research. This because it accurate and timely way to manage and classify with minimal human effort. In the computer vision community, deep-learning methods have become the most suitable approaches for this task. Anodes are large carbon blocks that are used to conduct electricity during the aluminum reduction process. The most basic function of anode rod inspection is to prevent a situation where the anode rod will not fit into the stub-holes of a new anode. It would be the case for a rod containing either severe toe-in, missing stubs, or a retained thimble on one or more stubs. In this work, to improve the accuracy of shape defect inspection for an anode rod, we use the Fast Region-based Convolutional Network method (Fast R-CNN), model. To train the detection model, we collect an image dataset composed of multi-class of anode rod defects with annotated labels. Our model is trained using a small number of samples, an essential requirement in the industry where the number of available defective samples is limited. It can simultaneously detect multi-class of defects of the anode rod in nearly real-time.
Artificial Intelligence-based Assistants AIAs are spreading quickly both in homes and offices. They already have left their original habitats of "intelligent speakers" providing easy access to music collections. The initiated a multitude of new devices and are already populating devices such as TV sets. Characteristic for the intelligent digital assistants is the formation of platforms around their core functionality. Thus, AIS capabilities of the assistants are used to offer new services and create new interfaces for business processes. There are positive network effects between the assistants and the services as well as within the services. Therefore, many companies see the need to get involved in the field of digital assistants but lack a framework to align their initiatives with their corporate strategies. In order to lay the foundation for a comprehensive method, we are therefore investigating intelligent digital assistants. Based on this analysis, we are developing a framework of strategic opportunities and challenges.
Predictive maintenance information systems: the underlying conditions and technological aspects
(2020)
Predictive maintenance has the potential to improve the reliability of production and service provisioning. However, there is little knowledge about the proper implementation of predictive maintenance in research and practice. Therefore, we conducted a multi-case study and investigated underlying conditions and technological aspects for implementing a predictive maintenance system and where it leads to. We found that predictive maintenance initiatives are triggered by severe impacts of failures on revenue and profit. Furthermore, successful predictive maintenance initiatives require that pre-conditions are fulfilled: Data must be available and accessible. Very important is also the support by the management. We identified four factors important for the implementation of predictive maintenance. The integration of data is highly facilitated by Cloud-based mechanisms. The detection of events is enabled by advanced analytics. The execution of predictive maintenance operations is supported by data-driven process automation and visualization.
New business opportunities appeared using the potential of the Internet and related digital technologies, like the Internet of Things, services computing, artificial intelligence, cloud, edge, and fog computing, social networks, big data with analytics, mobile systems, collaboration networks, and cyber-physical systems. Companies are transforming their strategy and product base, as well as their culture, processes and information systems to adopt digital transformation or to approach for digital leadership. Digitalization fosters the development of IT environments with many rather small and distributed structures, like the Internet of Things, Microservices, or other micro-granular elements. Digitalization has a substantial impact for architecting the open and complex world of highly distributed digital servcies and products, as part of a new digital enterprise architecture, which structure and direct service-dominant digital products and services. The present research paper investigates mechanisms for supporting the evolution of digital enterprise architectures with user-friendly methods and instruments of interaction, visualization, and intelligent decision management during the exploration of multiple and interconnected perspectives by an architecture management cockpit.
Enterprise Governance, Risk and Compliance (GRC) systems are key to managing risks threatening modern enterprises from many different angles. Key constituent to GRC systems is the definition of controls that are implemented on the different layers of an Enterprise Architecture (EA). Controls become part of a “concern” of the EA, which allows to use an EA viewpoint to cover control compliance assessments. In this article we explore this relationship further, derive a metamodel linking control and EA, and elicit how this linkage give rise to a hierarchic understanding of the viewpoint concept for EAs. We complement these considerations with an expository instantiation in a cockpit for control compliance applied in an international enterprise in the insurance industry.
Background: Design patterns are supposed to improve various quality attributes of software systems. However, there is controversial quantitative evidence of this impact. Especially for younger paradigms such as service- and microservice-based systems, there is a lack of empirical studies.
Objective: In this study, we focused on the effect of four service-based patterns - namely process abstraction, service façade, decomposed capability, and event-driven messaging - on the evolvability of a system from the viewpoint of inexperienced developers.
Method: We conducted a controlled experiment with Bachelor students (N = 69). Two functionally equivalent versions of a service-based web shop - one with patterns (treatment group), one without (control group) - had to be changed and extended in three tasks. We measured evolvability by the effectiveness and efficiency of the participants in these tasks. Additionally, we compared both system versions with nine structural maintainability metrics for size, granularity, complexity, cohesion, and coupling.
Results: Both experiment groups were able to complete a similar number of tasks within the allowed 90 min. Median effectiveness was 1/3. Mean efficiency was 12% higher in the treatment group, but this difference was not statistically significant. Only for the third task, we found statistical support for accepting the alternative hypothesis that the pattern version led to higher efficiency. In the metric analysis, the pattern version had worse measurements for size and granularity while simultaneously having slightly better values for coupling metrics. Complexity and cohesion were not impacted.
Interpretation: For the experiment, our analysis suggests that the difference in efficiency is stronger with more experienced participants and increased from task to task. With respect to the metrics, the patterns introduce additional volume in the system, but also seem to decrease coupling in some areas.
Conclusions: Overall, there was no clear evidence for a decisive positive effect of using service-based patterns, neither for the student experiment nor for the metric analysis. This effect might only be visible in an experiment setting with higher initial effort to understand the system or with more experienced developers.
Software evolvability is an important quality attribute, yet one difficult to grasp. A certain base level of it is allegedly provided by service- and microservice-based systems, but many software professionals lack systematic understanding of the reasons and preconditions for this. We address this issue via the proxy of architectural modifiability tactics. By qualitatively mapping principles and patterns of Service Oriented Architecture (SOA) and microservices onto tactics and analyzing the results, we cannot only generate insights into service-oriented evolution qualities, but can also provide a modifiability comparison of the two popular service-based architectural styles. The results suggest that both SOA and microservices possess several inherent qualities beneficial for software evolution. While both focus strongly on loose coupling and encapsulation, there are also differences in the way they strive for modifiability (e.g. governance vs. evolutionary design). To leverage the insights of this research, however, it is necessary to find practical ways to incorporate the results as guidance into the software development process.
A new class of information system architecture, decision-oriented service systems, is spreading more and more. Decision-oriented service systems provide services that support decisions in business processes and products based on the capabilities of cloud-computing environments. To pave the way for the creation of design methods of business processes and products based on decision-oriented service systems, this article introduces a capability-oriented approach. Starting from technological capabilities, more abstract operational and dynamic capabilities are created. The framework created is based on an integrated conceptualization of decision-oriented service systems that allows capturing synergetic effects. By creating the framework, the gap between the technological capabilities of technologies and the strategic goals of enterprises shall be narrowed.
The digitization of our society changes the way we live, work, learn, communicate, and collaborate. The Internet of Things, enterprise social networks, adaptive case management, mobility systems, analytics for big data, and cloud services environments are emerging to support smart connected products and services and the digital transformation. Biological metaphors of living and adaptable ecosystems provide the logical foundation for self-optimizing and resilient run-time environments for intelligent business services and service-oriented enterprise architectures. Our aim is to support flexibility and agile transformations for both business domains and related information technology. The present research paper investigates mechanisms for decision analytics in the context of multi-perspective explorations of enterprise services and their digital enterprise architectures by extending original architecture reference models with state of art elements for agile architectural engineering for the digitization and collaborative architectural decision support. The paper’s context focuses on digital transformations of business and IT and integrates fundamental mappings between adaptable digital enterprise architectures and service-oriented information systems. We are putting a spotlight on the example domain – Internet of Things.
Services Oriented Architectures (SOA) have emerged as a useful framework for developing interoperable, large-scale systems, typically implemented using the Web Services (WS) standards. However, the maintenance and evolution of SOA systems present many challenges. SmartLife applications are intelligent user-centered systems and a special class of SOA systems that present even greater challenges for a software maintainer. Ontologies and ontological modeling can be used to support the evolution of SOA systems. This paper describes the development of a SOA evolution ontology and its use to develop an ontological model of a SOA system. The ontology is based on a standard SOA ontology. The ontological model can be used to provide semantic and visual support for software maintainers during routine maintenance tasks. We discuss a case study to illustrate this approach, as well as the strengths and limitations.
Enterprise Architectures (EA) consist of a multitude of architecture elements, which relate in manifold ways to each other. As the change of a single element hence impacts various other elements, mechanisms for architecture analysis are important to stakeholders. The high number of relationships aggravates architecture analysis and makes it a complex yet important task. In practice EAs are often analyzed using visualizations. This article contributes to the field of visual analytics in enterprise architecture management (EAM) by reviewing how state-of-the-art software platforms in EAM support stakeholders with respect to providing and visualizing the “right” information for decision-making tasks. We investigate the collaborative decision-making process in an experiment with master students using professional EAM tools by developing a research study. We evaluate the students’ findings by comparing them with the experience of an enterprise architect.