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Managing components' resources plays a critical role in the success of systems' architectures designed for cyber–physical systems (CPS). Performing the selection of candidate components to pursue a specific application's needs also involves identifying the relationships among architectural components, the network and the physical process, as the system characteristics and properties are related.

Using a Model-Driven Engineering (MDE) approach is a valuable asset therefore. Within this context, the authors present the so-called Systems Architecture Ontology (SAO), which allows the representation of a system architecture (SA), as well as the relationships, characteristics and properties of a CPS application.

SAO uses a common vocabulary inspired by the Architecture Analysis and Design Language (AADL) standard. To demonstrate SAO's applicability, this paper presents its use as an MDE approach combined with ontology-based modeling through the Ontology Web Language (OWL). From OWL models based on SAO, the authors propose a model transformation tool to extract data related to architectural modeling in AADL code, allowing the creation of a components' library and a property set model. Besides saving design time by automatically generating many lines of code, such code is less error-prone, that is, without inconsistencies.

To illustrate the proposal, the authors present a case study in the aerospace domain with the application of SAO and its transformation tool. As result, a library containing 74 components and a related set of properties are automatically generated to support architectural design and evaluation.

It has become increasingly clear that the objectives of privacy and competition policy are in conflict with one another with regard to platform data. While privacy policies aim at limiting the use of platform data for purposes other than those for which the data were collected in order to protect the privacy of platform users, competition policy aims at making such data widely available in order to curb the power of platforms.

We draw on Commons' Institutional Economics to contrast the current control-based approaches to ensuring the protection as well as the sharing of platform data with an ownership approach. We also propose the novel category of platform use data and contrast this with the dichotomy of personal/non-personal data which underlies current regulatory initiatives.

We find that current control- and ownership-based approaches are ineffective with regard to their capacity to balance these conflicting objectives and propose an alternative approach which makes platform data saleable. We discuss this approach in view of its capacity to balance the conflicting objectives of privacy and competition policy and its effectiveness in supporting each separately.

Our approach clarifies the fundamental difference between data markets and other concepts such as data exchanges.

The world over is becoming urbanized, and people are migrating to cities in large numbers in search of opportunities. The increased urbanization has posed challenges such as congestion, rising crime, and growing urban poverty. The governments respond by providing amenities such as schools, hospitals, and housing to meet to increase in demand for these facilities. However, there is a need for the provision of facilities that meets the expectations of the people, particularly on the proximity of amenities and bundles of utility-bearing housing characteristics. In an attempt to address the challenge mentioned, the study estimated the hedonic characteristics influencing the willingness to accept and willingness to pay for housing facilities in the Eastern Cape Province, South Africa. Using a multiple linear regression model and artificial neural network, the study found out that properties with a bathroom, garage and large floor size have a higher value compared to properties without these facilities.When making decisions to acquire a property, buyers consider the availability of discounts and the prevailing property price. Overall, willingness to pay and accept decisions are mainly determined by location and the price at which homogeneous neighborhood properties were sold. Therefore, the study recommends that urban town planners and other housing authorities prioritize the construction of properties with larger floor areas, parking bays, and bathrooms using a cost-effective mechanism that makes the properties affordable to residents.

Artificial intelligence (AI) reasoning is fuelled by high-quality, detailed behavioural data. These can usually be obtained by the biometrical sensors embedded in smart devices. The currently used data collecting approach, where data ownership and property rights are taken by the data scientists, designers of a device or a related application, delivers multiple ethical, sociological and governance concerns. In this paper, the author is opening a systemic examination of a data sharing concept in which data producers execute their data property rights.

Since data sharing concept delivers a substantially different alternative, it needs to be thoroughly examined from multiple perspectives, among them: the ethical, social and feasibility. At this stage, theoretical examination modes in the form of literature analysis and mental model development are being performed.

Data sharing concepts, framework, mechanisms and swift viability are examined. The author determined that data sharing could lead to virtuous data science by augmenting data producers' capacity to govern their data and regulators' capacity to interact in the process. Truly interdisciplinary research is proposed to follow up on this research.

Since the research proposal is theoretical, the proposal may not provide direct applicative value but is largely focussed on fuelling the research directions.

For the researchers, data sharing concepts will provide an alternative approach and help resolve multiple ethical considerations related to the internet of things (IoT) data collecting approach. For the practitioners in data science, it will provide numerous new challenges, such as distributed data storing, distributed data analysis and intelligent data sharing protocols.

Data sharing may post significant implications in research and development. Since ethical, legislative moral and trust-related issues are managed in the negotiation process, data can be shared freely, which in a practical sense expands the data pool for virtuous research in social sciences.

The paper opens new research directions of data sharing concepts and space for a new field of research.

With revolutions in the telecommunication sector having led to wide unprecedented consequences in all facets of human life, this paper aims to examine the relationship between cell phone tower base stations (CPTBSs) and residential property prices within the City of Johannesburg (CoJ), South Africa.

The authors align their work with global literature and assess how the impact of CPTBSs influences residential property values in South Africa. The authors use a semi-log hedonic pricing model to test the hypothesis that proximity of CPTBSs to residential properties does not account for any variation in residential property prices.

The results show a significant impact that proximity of CPTBS has on residential property sale prices. However, the impact of CTPBSs’ proximity on residential property prices depends on their distance from the residential properties. The closer a residential property is to the CTPBS, the greater the impact that the CTPBS will have on the selling price of the residential property.

With international studies offering mixed findings on the impact of CPTBSs on residential property values, there is limited research on their impact in South Africa. The findings of this study offer crucial insights for the real estate practitioners, property owners, telecommunications companies and the public, providing a nuanced understanding of the relationship between CPTBSs and property values. This research helps property owners understand the effects of CPTBSs on their properties, and it assists property valuers in gauging the impact of CPTBSs on property values.

The purpose of this paper is to evaluate innovations in intellectual property rights (IPR) databases, techniques and software tools, with an emphasis on selected new developments and their contribution towards achieving advantages for IPR management (IPRM) and wider social benefits. Several industry buzzwords are addressed, such as IPR-linked open data (IPR LOD) databases, blockchain and IPR-related techniques, acknowledged for their contribution in moving towards artificial intelligence (AI) in IPRM.

The evaluation, following an original framework developed by the authors, is based on a literature review, web analysis and interviews carried out with some of the top experts from IPR-savvy multinational companies.

The paper presents the patent databases landscape, classifying patent offices according to the format of data provided and depicting the state-of-art in the IPR LOD. An examination of existing IPR tools shows that they are not yet fully developed, with limited usability for IPRM. After reviewing the techniques, it is clear that the current state-of-the-art is insufficient to fully address AI in IPR. Uses of blockchain in IPR show that they are yet to be fully exploited on a larger scale.

A critical analysis of IPR tools, techniques and blockchain allows for the state-of-art to be assessed, and for their current and potential value with regard to the development of the economy and wider society to be considered. The paper also provides a novel classification of patent offices and an original IPR-linked open data landscape.

Medical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is available for research only after the approval of ethics committees. The purpose of this research is to propose an architecture to support researchers to efficiently and effectively identify relevant collections of material and data with documented quality for their research projects while observing strict privacy rules.

Following a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research.

This study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRI-ERIC) and develops a conceptual model capturing schema information with quality annotation. This paper discusses the quality dimensions for data sets for medical research in-depth and proposes representations of both the metadata and data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies.

This novel conceptual model for metadata for medical data lakes has a unique focus on the high privacy requirements of the data sets contained in medical data lakes and also stands out in the detailed representation of data quality and metadata quality of medical data sets.

Currently, China’s economy is in the critical phase of transforming economic development patterns and replacing old growth drivers with new ones. Whether it can successfully overcome the “middle-income trap” has become a significant issue attracting wide attention.

Driven by underlying digital technologies such as artificial intelligence, blockchain, cloud computing and big data, the fourth industrial revolution featuring the booming digital economy has provided significant opportunities for China’s economy to “overtake” and overcome the “middle-income trap”. The transformation of economic development pattern, the optimization of industrial structure, and the change of growth drivers, brought by the deep integration of digital and real economies are the keys to leaping over the “middle-income trap”.

From the supply side, the digital economy can improve the quality and efficiency of the supply side and promote the supply-side structural reform and economic growth from the following three aspects: First, promote the quality, efficiency and diversification of the supply system; second, promote networking, opening-up and synergy in the innovation system and third, promote the socialization, modularization and flexibility of production pattern. From the demand side, the digital economy can boost the new drivers of the “troika” of economic growth consisting of consumption, exports and investment by changing the market investment direction, promoting consumption upgrade and fostering export strengths. However, once these two attributes interact with each other, especially when data is combined with capital, the most adhesive factor in the market economy, a series of new social relations will then be produced based on the technical attribute, resulting in significant adjustments in social relations, involving both positive and negative externalities.

To overcome the “middle-income trap”, it is necessary to adapt to the laws of economic evolution and promote a fundamental change in economic growth drivers; boost the high-quality development of the digital economy by strengthening the support role of data in the digital economy; and accelerate digital industrialization and industrial digitalization to realize the integration of digital and real economies.