(SAKM) Software Architecture Knowledge Management and its recent Practices, Models, Tools and Challenges

Management of knowledge for software architecture means to capture convenient experience and then translating it in generalized architectural knowledge. For refining the organizational architectural competences, architectural knowledge management is very much important. Architectural knowledge is valuable in the Software Architecture design process. This knowledge will help the stakeholders for communication in different phases of software development life cycle(SDLC). Properly managing the architectural knowledge is very much important as it is progressively more regarded the same as an organizational positive feature and that is why so many researchers around the world are proposing tools, methods, models and different frameworks for the effective knowledgemanagement [1]. This article contributes in exploring current work in field of software architectural knowledge management (AKM) from 2010 to 2017. This article highlights recent architectural AKM challenges and issues which are still not settled and here we also discuss different AKM tools, practicesand models

Comprehensive measurement framework for enterprise architectures

Enterprise Architecture defines the overall form and function of systems across an enterprise involving the stakeholders and providing a framework, standards and guidelines for project-specific architectures. Project-specific Architecture defines the form and function of the systems in a project or program, within the context of the enterprise as a whole with broad scope and business alignments. Application-specific Architecture defines the form and function of the applications that will be developed to realize functionality of the system with narrow scope and technical alignments. Because of the magnitude and complexity of any enterprise integration project, a major engineering and operations planning effort must be accomplished prior to any actual integration work. As the needs and the requirements vary depending on their volume, the entire enterprise problem can be broken into chunks of manageable pieces. These pieces can be implemented and tested individually with high integration effort. Therefore it becomes essential to analyze the economic and technical feasibility of realizable enterprise solution. It is difficult to migrate from one technological and business aspect to other as the enterprise evolves. The existing process models in system engineering emphasize on life-cycle management and low-level activity coordination with milestone verification. Many organizations are developing enterprise architecture to provide a clear vision of how systems will support and enable their business. The paper proposes an approach for selection of suitable enterprise architecture depending on the measurement framework. The framework consists of unique combination of higher order goals, non-functional requirement support and inputs-outcomes pair evaluation. The earlier efforts in this regard were concerned about only custom scales indicating the availability of a parameter in a range.Comment: 22 Page

A governance approach for bim management across lifecycle and supply chains using mixed-modes of information delivery

Built environment data is of varying nature embedding various forms of sensitivities with potential legal, contractual, intellectual property, and security implications. The paper presents a governance approach for managing multi-actor, multi-discipline, and total lifecycle data, informed by a wide industry consultation conducted in the UK between March and September 2011. The study identifies a number of barriers in engaging with Building Information Modelling (BIM) efforts with a view of facilitating collaboration around a common and integrated project specification. A governance model is proposed that addresses the identified adoption blockers underpinned by a “mixed approach”, that factors in various modes of information delivery, ranging from paper-based documents to object-based information conveyed by IFC (Industry Foundation Classes). A demonstrator system is developed and used to validate our BIM governance concepts. Our governance model is discussed in the context of the recent UK government BIM industry consultation document supported by a research and development (R&D) roadmap taking into account current industry structure and its various levels of stakeholders’ maturity, capability and readiness

Data integration and FAIR data management in Solid Earth Science

Integrated use of multidisciplinary data is nowadays a recognized trend in scientific research, in particular in the domain of solid Earth science where the understanding of a physical process is improved and made complete by different types of measurements – for instance, ground acceleration, SAR imaging, crustal deformation – describing a physical phenomenon. FAIR principles are recognized as a means to foster data integration by providing a common set of criteria for building data stewardship systems for Open Science. However, the implementation of FAIR principles raises issues along dimensions like governance and legal beyond, of course, the technical one. In the latter, in particular, the development of FAIR data provision systems is often delegated to Research Infrastructures or data providers, with support in terms of metrics and best practices offered by cluster projects or dedicated initiatives. In the current work, we describe the approach to FAIR data management in the European Plate Observing System (EPOS), a distributed research infrastructure in the solid Earth science domain that includes more than 250 individual research infrastructures across 25 countries in Europe. We focus in particular on the technical aspects, but including also governance, policies and organizational elements, by describing the architecture of the EPOS delivery framework both from the organizational and technical point of view and by outlining the key principles used in the technical design. We describe how a combination of approaches, namely rich metadata and service-based systems design, are required to achieve data integration. We show the system architecture and the basic features of the EPOS data portal, that integrates data from more than 220 services in a FAIR way. The construction of such a portal was driven by the EPOS FAIR data management approach, that by defining a clear roadmap for compliance with the FAIR principles, produced a number of best practices and technical approaches for complying with the FAIR principles. Such a work, that spans over a decade but concentrates the key efforts in the last 5 years with the EPOS Implementation Phase project and the establishment of EPOS-ERIC, was carried out in synergy with other EU initiatives dealing with FAIR data. On the basis of the EPOS experience, future directions are outlined, emphasizing the need to provide i) FAIR reference architectures that can ease data practitioners and engineers from the domain communities to adopt FAIR principles and build FAIR data systems; ii) a FAIR data management framework addressing FAIR through the entire data lifecycle, including reproducibility and provenance; and iii) the extension of the FAIR principles to policies and governance dimensions.publishedVersio

Enhancing Information Governance with Enterprise Architecture Management: Design Principles Derived from Benefits and Barriers in the GDPR Implementation

Businesses today are increasingly dependent on how they transform information into economic value, while simultaneously being compliant with intensified privacy requirements, resulting from legal acts like the General Data Protection Regulation (GDPR). As a consequence, realizing information governance has become a topic more important than ever to balance the beneficial use and protection of information. This paper argues that enterprise architecture management (EAM) can be a key to GDPR implementation as one important domain of information governance by providing transparency on information integration throughout an organization. Based on 24 interviews with 29 enterprise architects, we identified a multiplicity of benefits and barriers within the interplay of EAM and GDPR implementation and derived seven design principles that should foster EAM to enhance information governance

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

The impact of data governance on corporate performance : the case of a petroleum company

Includes bibliographical references.While it is acknowledged that data is a valuable corporate asset, many companies fail to exploit it in order to better their performance. Organizations today need to be proactive in their operations and have to make informed business decisions in less time than ever before. This puts pressure on the organisations to better govern the use of data within an organization. Literature has shown that a holistic conceptualization of factors affecting data governance is missing. Also there is limited research on the effects of data governance on firm performance. This study therefore seeks to fill this gap by investigating the factors that affect data governance in organization X which operates in the petroleum industry and also determine the extent to which the quality of data governance influences its corporate performance. A conceptual model derived from the literature review was used to guide this study. Data was collected from 50 employees in organisation X whose job descriptions are aligned with data management via an intranet web based survey. Quantitative methods were then used to analyse the data. Results of the regression analysis confirmed four out of six research propositions made. Compliance with data policies and regulations, data stewardship and ownership were not found to be significant predictors of data governance. However, data modeling, data integration and data quality are necessary in order to achieve improved data governance. The present study also confirms that poor data governance has a negative impact on corporate performance suggesting that organisation X needs to enhance the quality of data governance in order to realise its full business value and also improved business performance

A Reference Architecture for Service Lifecycle Management – Construction and Application to Designing and Analyzing IT Support

Service-orientation and the underlying concept of service-oriented architectures are a means to successfully address the need for flexibility and interoperability of software applications, which in turn leads to improved IT support of business processes. With a growing level of diffusion, sophistication and maturity, the number of services and interdependencies is gradually rising. This increasingly requires companies to implement a systematic management of services along their entire lifecycle. Service lifecycle management (SLM), i.e., the management of services from the initiating idea to their disposal, is becoming a crucial success factor. Not surprisingly, the academic and practice communities increasingly postulate comprehensive IT support for SLM to counteract the inherent complexity. The topic is still in its infancy, with no comprehensive models available that help evaluating and designing IT support in SLM. This thesis presents a reference architecture for SLM and applies it to the evaluation and designing of SLM IT support in companies. The artifact, which largely resulted from consortium research efforts, draws from an extensive analysis of existing SLM applications, case studies, focus group discussions, bilateral interviews and existing literature. Formal procedure models and a configuration terminology allow adapting and applying the reference architecture to a company’s individual setting. Corresponding usage examples prove its applicability and demonstrate the arising benefits within various SLM IT support design and evaluation tasks. A statistical analysis of the knowledge embodied within the reference data leads to novel, highly significant findings. For example, contemporary standard applications do not yet emphasize the lifecycle concept but rather tend to focus on small parts of the lifecycle, especially on service operation. This forces user companies either into a best-of-breed or a custom-development strategy if they are to implement integrated IT support for their SLM activities. SLM software vendors and internal software development units need to undergo a paradigm shift in order to better reflect the numerous interdependencies and increasing intertwining within services’ lifecycles. The SLM architecture is a first step towards achieving this goal.:Content Overview List of Figures....................................................................................... xi List of Tables ...................................................................................... xiv List of Abbreviations.......................................................................xviii 1 Introduction .................................................................................... 1 2 Foundations ................................................................................... 13 3 Architecture Structure and Strategy Layer .............................. 57 4 Process Layer ................................................................................ 75 5 Information Systems Layer ....................................................... 103 6 Architecture Application and Extension ................................. 137 7 Results, Evaluation and Outlook .............................................. 195 Appendix ..........................................................................................203 References .......................................................................................... 463 Curriculum Vitae.............................................................................. 498 Bibliographic Data............................................................................ 49

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical