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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.

The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making.

A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed.

Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics.

The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties.

The proposed model has the potential to avoid the mistake from a misleading data imputation.

The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations.

This is the first time that the whole data analytics is considered from the point of view of grey systems.

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.

The purpose of this work is to investigate industrial asset management (AM) in manufacturing. After depicting gaps for AM in this sector, the role of information as a key dimension is considered to realise a summary of challenges and advices for future development.

The work is grounded on an extensive systematic literature review. Considering the eligible documents, descriptive statistics are provided and a content analysis is performed, both based on a sector-independent normative-based framework of analysis.

AM principles, organisation and information are the dimensions defined to group ten areas of interest for AM in manufacturing. Information is the major concern for an effective AM implementation. Moreover, Internet of Things and big data management and analytics, as well as data modelling and ontology engineering, are the major technologies envisioned to advance the implementation of AM in manufacturing.

The identified challenges and advices for future development may serve to stimulate further research on AM in manufacturing, with special focus on information and data management. The sector-independent normative-based framework may also enable to analyse AM in different contexts of application, thus favouring cross-sectorial comparisons.

Industries with higher operational risk, like Oil&Gas and infrastructure, are advanced in AM, while others, like some in manufacturing, are laggard in this respect. This literature review is the first of a kind addressing AM in manufacturing and depicts the state-of-the-art to pave the way for future research and development.

The emergence of distributed manufacturing (DM) is examined as a new form of localised production, distinct from previous manifestations of multi-domestic and indigenous production.

Supply network (SN) configuration and infrastructural provisioning perspectives were used to examine the literature on established localised production models as well as DM. A multiple case study was then undertaken to describe and explore the DM model further. A maximum variation sampling procedure was used to select five exemplar cases.

Three main contributions emerge from this study. First, the research uniquely brings together two bodies of literature, namely SN configuration and infrastructure provisioning to explore the DM context. Second, the research applies these theoretical lenses to establish the distinctive nature of DM across seven dimensions of analysis. Third, emerging DM design rules are identified and compared with the more established models of localised production, drawing on both literature and DM case evidence.

This study provides a rich SN configuration and infrastructural provisioning view on DM leading to a set of design rules for DM adoption, thus supporting practitioners in their efforts to develop viable DM implementation plans.

The authors contribute to the intra- and inter-organisational requirements for the emerging DM context by providing new perspectives through the combined lenses of SN configuration and infrastructural provisioning approaches.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

This paper aims to raise awareness of a potential planning pitfall and provide recommendations on how to assess and improve upon current practices. In potential conflict areas, such as the Korean Theater of Operations (KTO), military forces are required to store a portion of their ammunition combat load within depots and ammunition supply points under the control of a servicing ammunition company. This necessitates a lengthy retrieval process, as the ammunition company does not have enough resources to serve all customers simultaneously.

The stored combat load (SCL) retrieval process is modeled as a parallel machine scheduling problem and simulated using synthetic requirements. The current system of retrieval is contrasted against a proposed alternate system through a series of simulations scaled across three factors: number of ammunition company Soldiers, number of customer units and number of magazines.

The proposed alternate system demonstrates a significant potential for reducing the makespan of the SCL retrieval process when more than half of the magazines store SCL for multiple customers and there are more than five customers per Soldier.

Transitioning military units from a peacetime standing to full combat readiness as quickly as possible is of immense value within the KTO and other hostile areas with established troops not actively engaged in combat.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.

The purpose of this paper is to demonstrate the relevance of business intelligence (BI) in businesses in general and tourism firms in particular. BI has been hailed as an innovation that can propel businesses that adopt the system to high productivity and efficiency. This paper confirms that view but further adds that BI also enhances a business’s competitiveness.

This paper reviews literature on the use of BI in tourism. Although current literature is largely fragmented, focusing on BI, the tourism industry and the notion of competitiveness separately, this paper makes an attempt to bring the three sub-themes in the same study and highlights their interconnectedness. The study adopts two environmental analysis models to better analyze this matter. First is the environmental analysis model as based on Downes’s modification of Porter’s five forces framework. The second model used is the resource-based view approach to business environmental analysis.

This paper affirms that the tourism industry is one of those industries that continue to benefit from the advantages that come with the adoption of a BI system. Literature shows that the tourism industry was one of those that first adopted BI in order to benefit from the benefits that come with its adoption. Such advantages include flexible and user friendly tourists’ data capture, storage, retrieval, processing and analytical capabilities.

This was a largely literature review-based study. There is, therefore, room for strengthening its findings by conducting field work and mixed methods research for more robust results.

This study will surely benefit the tourism industry and business in general from its highly favorable conclusions to the benefits that come with the adoption of a BI system. It can also be used as a reference in to the tourism field, especially aggregating important concepts and literature that can help future practical studies.

Society will also benefit from this study in terms of the new knowledge that has been generated. Members of society will then be in a position to demand products and services that are a result of innovation and informed decision making.

Although this paper is largely based on literature, the conclusions reached are those of the authors. A close assessment of the literature in BI and the tourism industry was done, resulting in the conclusions reached by the authors.

The purpose of this paper is to construct a digital collection and database of traditional clothing that is convenient for the digital dissemination and application of traditional clothing and provide resources for research on clothing fashion, traditional clothing techniques, clothing culture, history and clothing teaching.

A real object analysis method was used in this paper, based on 15 core elements of the internationally common DC metadata standard, and with consideration to the characteristics of clothing products and clothing industry application specifications, the core elements of DC are expanded to facilitate the detailed record of the characteristic information of clothing, especially the implicit clothing culture. A code symbol compilation method was developed to give each piece of clothing a unique number, facilitating identification, classification and recording. At last, a metadata construction scheme for traditional clothing was developed. A traditional embroidered children's hat and Mamianqunt serve as examples to demonstrate the metadata elements.

The clothing meta-database provides a main body of traditional clothing while also paying attention to the collection of cultural elements. It is composed of five layers of classified data, source data, characteristic data, connotation data and management data, as well as 28 data elements, providing ease of sharing and interoperation.

This paper expands the subset of fashion metadata by describing traditional clothing metadata, especially the excavation of clothing cultural elements, and developing code compilation methods so that each clothing product can obtain a unique identification number, thereby building a traditional clothing metadata construction scheme consisting of five data layers and containing 28 data elements. This scheme records the information about each layer of traditional clothing in detail and provides shared data for discipline research and industry applications.