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From the moment a man took ‘the king’s shilling’ and was sworn to serve as a soldier in the nineteenth-century British Army, his life proceeded as a file as well as a fighting man. Disorder and desertion drove the utilitarian purposes of discipline and tracking, while constant pressure to account for expenditure in lives and money added further impetus to the copious industry of military record-keeping. Individuals were enumerated, named, appraised and allocated pay. Such archives produce a disorderly silence where men are present but without voice. Carefully archived and always public, military files have a continuing currency through the post-army lives of soldiers into the twenty-first century for descendants and historians. Tracking the life of ‘files’ over time, the paper reflects on the shifting forms of knowledge produced. In particular, it notes the tensions between the densely written form of the files in a population of rank and file soldiers who were partially literate; the highly detailed individuation of the files within a heavily conformist institution, and the modernity of post-1850s record-keeping in an institution bound by tradition. It ends with a reflection on the limitations and opportunities presented by digital access to this substantial archive of imperial-colonial conflict. Abbreviations: AJCP: Australian Joint Copying Project TNA: The National Archives, London WO: War Office [ABSTRACT FROM AUTHOR]

This paper examines the work practices involved in making data legible to machines and machine output legible to humans. The study is based on ethnographic research of a team of art experts at DNArt – a data classification system that features a growing database of art images, a classification scheme, a similarity matching algorithm, and a website that together serve as a consumer judgment device in an emerging online market for art. I analyze interactions from meeting observations, interviews, documentation, and online interaction data to show how non-technical art experts explain and repair sociotechnical breakdowns – when their expectations for similarity between art images and artists differ from the similarity relations produced by the algorithm. By repairing breakdowns, the art experts construct the algorithm anew, as a legitimate revealer of similarity in art. In doing so, the team's repair work is folded back into the black box of the algorithm, rendering it invisible and unacknowledged, sometimes even by the experts themselves. [ABSTRACT FROM AUTHOR]

New technology refers to the use of computing machines, AI, Big Data, IoT, deep ‎‎learning, IT, MIS, ‎AIS, knowledge management, capture, manipulate, ‎and retrieve shared knowledge. Therefore, the integration of modern technology, ‎entrepreneurship, and business should be well ‎managed to provide a wide range of high-quality ‎and competitive products and services in societies. The aim of this special issue is to highlight the latest features that blend AI, Big Data, and IoT facilitated and employ them to support the successful growth of businesses. The target of this special issue is to accept high-quality scientific articles that express theory and practical conceptualizations of ideas and critical surveys that cover all aspects pertaining to IoT, AI, and Big Data and their relationship to business success. The special issue received 36 papers, some of which were presented in ICBT'2021 and CBF'2022. All of them were desk evaluated by the editors, followed by at least two blind reviews. [ABSTRACT FROM AUTHOR]

The article presents the volume 18, special issue of the number 3, 2016 on the journal, highlighting the special issue concerning big data analytics.

Production and distribution are two key constituents of a supply chain. In view of the growing availability of data and advances in big data analytics techniques, there have been more and more applications of data analytics to deal with the problems in production and distribution management. With this in mind, we proposed a special issue on 'Big Data Analytics in Production and Distribution Management' to report the latest development in this field. In this editorial, we first introduce the background and examine the existing review works on the applications of data analytics to operations management. We then introduce the papers accepted in the issue, and discuss how different types of big data analytics techniques are applied to production and distribution management, including demand forecasting, production scheduling, distribution management, manufacturing management, and supply chain management. Finally, we conclude the paper with a discussion of future research. [ABSTRACT FROM AUTHOR]

This collection on 'Artificial intelligence, big data, algorithms and Industry 4.0 in firms and clusters' is introduced exploring the themes discussed by the nine papers and grouped into three categories to uncover new dynamics and identify future research opportunities for clusters and organizations in these transformative times. The first group explores theoretical aspects of AI and its evolution in social sciences, focusing on industry 4.0, smart cities, big data, and other related topics. The second group examines the role of industrial robots in employment, productivity, and knowledge absorption in industrial districts. The third group discusses innovation in the context of local production systems, AI ecosystems, and the growth and potential of the Metaverse. [ABSTRACT FROM AUTHOR]

While individuals living near or on archaeological sites have frequently been hired around the world to dig on archaeological excavations, they have very rarely participated in the recording or documentation of those excavations. They have played even less of a role in designing the structures of either paper or electronic data management systems. In this paper, I describe some potential gaps in the archaeological record as a result of this exclusion, by detailing some ways that the communities at Çatalhöyük, Turkey and Petra, Jordan have developed highly situated forms of knowledge about these archaeological sites due to their proximities to them. I also argue that "proximity" inculcates not only forms of knowledge about an archaeological site, but also, under certain conditions, an important means of sharing knowledge between archaeologists and the communities who live where we work. I contrast proximity to the expansiveness of big data, and question whether it is possible and even preferable to imagine ways of integrating local, proximate perspectives into the rubric of big data. [ABSTRACT FROM AUTHOR]

The purpose of this study is to review journal papers on educational big data research published from 2010 to 2018. A total of 143 papers were selected. The papers were characterized based on three dimensions: (a) educational goals; (b) educational problems addressed; and (c) big data analytical techniques used. A qualitative content analysis approach was conducted to develop a coding scheme for analyzing the selected papers. The results identified four types of educational goals, with a clear predominance of quality assurance. The identification of the most mentioned educational problems resulted in four main concerns: the lack of detecting student behavior modeling and waste of resources; inappropriate curricula and teaching strategies; oversights of quality assurance; and privacy and ethical issues. Concerning the most mentioned big data analytical techniques, the coding scheme revealed that the majority of the papers focused on the educational data mining technique followed by the learning analytics technique. The visual analytics technique was mentioned in a few papers. The results also indicated that the educational data mining technique is the most suitable technique to use for quality assurance and to provide potential solutions for the lack of detecting student behavior modeling and the waste of resources in institutions. [ABSTRACT FROM AUTHOR]

It is a widely accepted fact that almost every research or business revolves around Data. Data from various business sectors has been growing sharply and the management of this massive amount of data is the biggest professional crunch these days. The notion of Big Data Analytics (BDA) is a prominent facet that delivers the best possible solution to decision-makers for efficiently handling the problems related to huge data. The key role of BDA in the area of Supply Chain Management (SCM), Logistics Management (LM), and Inventory Management (IM) is of utmost significance as it optimises the business operations by analyzing customer behaviour. Motivated with the promising paybacks of the BDA, a recent review from the year 2015–2019 is presented in this paper. Further, the significance of BDA in SCM, LM, and IM has been highlighted by studying 58 papers, which have been sorted after a detailed study of 260 papers, collected through the Web of Science (WoS) database. The findings and observations give state-of-the-art insights to scientists and business professionals by presenting an exhaustive list of the progress made and challenges left untackled in the field of BDA in SCM, LM, and IM. [ABSTRACT FROM AUTHOR]

In recent years, with the ageing of the population, environmental degradation and dietary abnormalities, the incidence of global cardiovascular disease has increased sharply, its serious impact on physical and mental health of humans is an important factor. Chinese medicine has a long history and significant efficacy in the treatment of cardiovascular disease. Based on the bibliometric method in the context of big data, this paper searched and screened the relevant literatures of TCM in the treatment of cardiovascular diseases included by SCI from 2004 to 2016 and analysed them from the aspects of annual change, regional distribution, institutional distribution, journal source, research field, highly cited literatures, high-frequency keywords and so on. The analysis results show that the total amount of literature in the treatment of cardiovascular diseases by traditional Chinese medicine is on the rise, mainly focusing on the application of traditional Chinese medicine and Chinese herbal compound, acupuncture and moxibustion, and taiji and other traditional Chinese medicine regimen. The research results of this paper provide a direction reference for TCM researchers to better understand the current research status of TCM in the treatment of cardiovascular diseases under the background of big data and for further research. [ABSTRACT FROM AUTHOR]

In the past few years, the field of big data has multiplied, with more academic papers written about it. This bibliometric research was done to look at and understand the trends regarding countries, organizations, authors, and keywords that are creating the most publications and citations in big data. This study was done to understand the current state of scientific publications in the field. The research used Web of Science (WoS) database information from 1993 to 2021. The study of 32,085 papers showed that, on average, each document has 14.7 citations and 3.46 citations per year. According to the results, the United States, China, and the United Kingdom have the most scholarly publications about big data. The Chinese Academy of Science, Harvard University, and Stanford University were the three most productive groups. When researching big data, most writers work together, and most terms are related to big data analytics, machine learning, and cloud computing. [ABSTRACT FROM AUTHOR]

Modern artificial intelligence (AI) is heavily reliant on deep learning (DL), an emerging class of algorithms that can automatically detect non-trivial patterns from petabytes of rapidly evolving "Big Data." Although the information systems (IS) discipline has embraced DL, questions remain about DL's interface with a domain and theory and DL contribution types. In this paper, we present a DL information systems research (DL-ISR) schematic that reviews DL while considering the role of the application environment and knowledge base, summarizes extant DL research in IS, a knowledge contribution framework (KCF) to position DL contributions, and ten guidelines to help IS scholars design, execute, and present DL for computational, behavioral, or economic IS research. We illustrate a research contribution to DL for cybersecurity. This article's contribution to theory resides in the conceptual DL-ISR schematic and KCF, while its contributions to practice are based on its practical guidelines for executing DL-based projects. [ABSTRACT FROM AUTHOR]

This paper provides a literature review of the research within the framework of 1) analytics,2) supply chain management, and 3) enterprise information systems, and relate the findings to competitive enablers. The findings are used to construct a future research agenda. The methodology is a systematic two-stage approach, based on a Smart Literature review framework using topic modelling. The research agenda proposes future research within the themes of 1) context, 2) cross-functional analytics, 3) cross-planning level analytics, 4) implementation and assimilation of analytics in EIS, 5) analytics and big data for SCM, 6) managerial aspects of analytics, and 7) data and system heterogeneity. [ABSTRACT FROM AUTHOR]

Given their potential, Big Data and Analytics can help institutions of higher education to thoroughly examine newly emerging challenges, explore and identify new ways to address them, and predict future outcomes for growth. Considering how new Big Data and Analytics are, existing knowledge about the potential value to higher education is limited. To bridge this gap, a modified version of Porter's Value Chain Model is utilised to explain how Big Data and Analytics improve value creation in higher education. The paper demonstrates, using real examples, how early adopters utilised the potential of Big Data and Analytics to improve several primary activities along the higher education value chain. While contributing to the academic literature, this paper provides insightful practical implications. These insights can help higher education leaders, administrators, and senior managers deal with newly emerging challenges and plan the strategic direction of their institutions. [ABSTRACT FROM AUTHOR]

Quality 4.0 is a new term representing a new featured approach to quality management. The recent development in information and communication technologies resulted in a great leap in Industry, causing the emergence of what is called 'Industry 4.0'. The term '4.0' has been attached to other fields such as Quality 4.0, Agriculture 4.0, Agribusiness 4.0, Service 4.0, Logistics 4.0, Health 4.0. etc., all representing the impact of Industry 4.0 on these terms. In the meanwhile, Quality 4.0 hasn't been adequately discussed from a scientific perspective; few journal articles pointed out 'Quality 4.0' but without a comprehensive interpretation for the scientific foundations behind it. Therefore, this paper suggested a comprehensive, hybrid and balanced definition for Quality 4.0. The paper suggested that Quality 4.0 is an extended approach to quality management, where recent technologies are being integrated with traditional quality practices (QC, QA, TQM) to expand the quality management scope and to improve quality activities. The paper also identified Quality 4.0 features, technologies, and applications. Moreover, the paper highlighted the challenges and future research topics in the context of Quality 4.0. [ABSTRACT FROM AUTHOR]

The question this special issue would like to address is how to harvest big data to help decision-makers to deliver better fact-based decisions aimed at improving performance or to create better strategy? This special issue focuses on the big data applications in supporting operations decisions, including advanced research on decision models and tools for the digital economy. Responds to this special issue was great and we have included many high-quality papers. We are pleased to present 13 of the best papers. The techniques presented include data mining, simulation and expert system with applications span across online reviews, food retail chain to e-health. [ABSTRACT FROM AUTHOR]

The use of big data is growing in relevance and importance in tourism management research. Companies operating in this industry are exploiting big data analytics and developing systems to manage customer knowledge and provide the best service in the right place at the right time. This paper aims to provide a systematic literature review to present issues associated with the use of big data in tourism and identify future research directions on the topic. To achieve this aim, this paper develops a citation network analysis methodology to drive the content analysis and explore the content of 109 selected papers. The findings of this review highlight that although there is an increasing number of contributions on the topic, there are yet some issues that require to be further developed. In particular, the paper identifies research gaps and consequent research questions that represent an agenda for both researchers and practitioners. [ABSTRACT FROM AUTHOR]

This study aims to provide a contribution to the systematisation of the state-of-the art of the literature on Big Data for the process of New Product Development (NPD). Based on the evidence of a structured literature review (SLR) on articles published from 2015 to 2021, the paper aims to identify new areas for future research, by highlighting the contribution of Big Data in the perspective of knowledge management for the improvement of the new product development process. Findings demonstrate a lack of research in this field and the fragmentation of the publications that require more in-depth investigation. The analysis allows the identification of quantitative and qualitative evidence of the research trends emerging at the intersection of the two well-known areas and to derive several implications both for research and practice. [ABSTRACT FROM AUTHOR]

This paper mainly studies the financial data management method and edge computing platform based on intelligent edge computing and big computing. This paper first introduces the theoretical basis of edge computing and big data computing, analyzes the collaborative mode of edge computing model and cloud computing, and discusses the cloud data storage scheme. In this paper, edge computing platform is built, and EC Master and Docker private image warehouse are mainly deployed in the data center. In this paper, the edge computing platform constructed was tested. In the mobile edge computing business processing time test, when there was a big gap in computing amount between business 1 and business 2, the total service time of MEC instances was around 0.3 s, showing a good user experience. In the edge acceleration effect test, before and after the adoption of CDN edge acceleration, from the perspective of the overall web page, the average time consumed to open a web page decreased from 1615 ms to 403 ms. From requesting a single HTTP access request, the average time decreased from 520 ms to 74 ms. The financial data management edge computing platform designed in this paper can effectively manage financial data and make an effective contribution to enterprise data management. [ABSTRACT FROM AUTHOR]

The paper approaches preventive justice and big data from a predictive policing point of view. Predictive policing is a controversial technology because of the many risks it imposes on fundamental rights. Through use cases from the EU, the paper focuses on how the implementation of predictive policing technologies has been limited in the EU but also how difficult the limitations might be due to the high hopes set upon technology. Although predictive policing technologies are not yet used in Finland by the police, the paper discusses how the use of police powers is limited in Finland and how fundamental rights control in the democratic process will work if the police were willing to adopt such technology. [ABSTRACT FROM AUTHOR]

With the development of information technology, big data analysis has been highlighted in operations and management. From this viewpoint, this paper studies a buyer's optimal purchasing decisions in combined procurement. For combined procurement, a buyer first signs a long-term contract with a supplier to guarantee a certain level of commodity supply, and can then replenish the commodities from the spot market if necessary. The optimal purchasing quantity in the long-term contract is examined to maximise the buyer's expected profit from combined procurement. In view of the imperfectness in the spot market, the spot trading liquidity is considered in the buyer's optimal purchasing decision. The properties of the two optimal purchasing quantities are examined and several interesting results are obtained. For example, it is illustrated that a buyer's expected profit may decrease in the spot capacity, a result that has never appeared in the existing literature, which reveals the importance of a buyer's optimal order decision in the presence of spot replenishment. Numerical results and sensitivity analysis are performed to verify the results. Management insights are suggested for a buyer's optimal purchasing decisions in combined procurement with a long-term contract and spot replenishment. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the impact of enterprise architecture (EA) on system capabilities in dealing with changes and uncertainties in globalised business environments. Enterprise information systems are viewed as information systems to acquire, process, and utilise data in decision-making supports at all levels and domains of businesses, and Internet of things (IoT), big data analytics (BDA), and digital manufacturing (DM) are introduced as representative enabling technologies for data collection, processing, and utilisation in manufacturing applications. The historical development of manufacturing technologies is examined to understand the evolution of system paradigms. The Shannon entropy is adopted to measure the complexity of systems and illustrate the roles of EAs in managing system complexity and achieving system stability in the long term. It is our argument that existing EAs sacrifice system flexibility, resilience, and adaptability for the reduction of system complexity; note that higher adaptability is critical to make a manufacturing system successfully. New EA is proposed to maximise system capabilities for higher flexibility, resilience, and adaptability. The potentials of the proposed EA to modern manufacturing are explored to identify critical research topics with illustrative examples from an application perspective. [ABSTRACT FROM AUTHOR]

In order to improve the effect of urban road congestion analysis, this paper aims to study the mechanism of traffic congestion diffusion under the condition that users have real-time traffic information, and analyze the urban road congestion situation combined with big data visualization technology. According to the characteristics of traffic flow propagation, this paper conducts multi-granularity abstraction and multi-scale modeling of node-intersection-link-network for the complex and dynamic traffic congestion process, and establishes an improved SIS virus propagation model for traffic congestion propagation. In addition, this paper uses the method of state transition probability to construct an interactive dynamic model of traffic congestion propagation and early warning information propagation in a multi-layer network. The experimental research results show that the big data visualization technology introduced in this paper can play an important role in urban road congestion. [ABSTRACT FROM AUTHOR]

Remote sensing technology in the new era gradually need a new scientific paradigm driven by big data and knowledge. However, in the current research, the use of existing knowledge is slightly insufficient. As for the whole research process, there is a lack of guidance based on the field knowledge. In view of the above problems in the field of remote sensing in agriculture, it is of great significance to organize knowledge based on concept graph and develop knowledge-driven big data analysis theory and methods. This paper focuses on the construction and application of agricultural remote sensing concept graph. Firstly, it explores the core contents, research hotspots and development trends of the current agricultural remote sensing field through reference visualization and analysis methods such as keyword frequency statistics, keyword co-occurrence, word detection with stronger citation burst and so on. Then, this paper summarizes the comprehensive ontology of agricultural remote sensing based on the results of keyword clustering. Thus, the schema layer of concept graph is constructed from top to bottom. Under the guidance of the schema layer, the data layer of the concept graph is artificially filled from the bottom up with different triads stored in Neo4j. In terms of application, retrieval, query and recommendation of the proposed concept graph for agricultural remote sensing tasks is a typical application case. Based on the results of the recommendation, it could guide the process of information extraction such as cultivated land parcel delineation and crop type identification at the parcel scale. The study of concept graph helps to summarize and generalize the relevant knowledge in the field of agricultural remote sensing. Relevant research provide effective solutions for automated and intelligent agricultural information extraction and application. [ABSTRACT FROM AUTHOR]

What constitutes good intelligence is best understood by practitioners but has not been explored through empirical analysis and in the context of a digital age. This paper presents the first research inside all the agencies that form the Australian National Intelligence Community exploring how they are impacted by big data. Intelligence is often opaque to outsiders, yet understanding the characteristics of good intelligence is important to societies that rely on intelligence agencies for national security. This paper reflects the previously unheard perspectives of members of the agencies that form the Australian National Intelligence Community – where there is a significant empirical gap. Semi-structured interviews with 47 participants explored the impact of big data on intelligence and decision-making in Australia. This paper finds that intelligence must meet the following characteristics, many established in historical literature, in order to be considered good intelligence; (i) timely, (ii) purposeful, (iii) actionable, (iv) accurate, (v) provides value-add for an intended audience, and, (vi) is unbiased. This article explores and unpacks each of these characteristics of good intelligence and finds they remain critical in a big data era. [ABSTRACT FROM AUTHOR]

Big data analytics (BDA) is of paramount importance in healthcare aspects such as patient diagnostics, fast epidemic recognition, and improvement of patient management. The objective of this profiling study is (a) to provide an overview of the BDA publication dynamics in the healthcare domain and (b) to discuss this scientific field through related examples. A sampling literature review has been conducted. A total of 804 papers have been identified and content analysis has been performed to mine knowledge in the domain for the years 2000–2016. The findings show that co-authors' backgrounds are from the subject areas of medicine and computer sciences. Most articles are experimental in nature and use modeling and machine learning techniques to exploit clinical data, for health monitoring and prediction purposes. Many articles are relevant to the medical specialties of neurology/neurosurgery/neuropsychiatry, medical oncology, and cardiology. Well-cited papers investigate the identification and management of high-risk/cost patients, the use of big data, Hadoop and cloud computing in genomics, and the development of mobile applications for disease management. Important is also the research about improving disease prediction by investigating patients' medical results using advanced analysis (such as segmentation and predictive modelling, machine learning, visualisation, etc.). [ABSTRACT FROM AUTHOR]

Organizations have long valued and pursued data analytics. In this way, this paper underscores the findings of the second paper that advocates investment in data analytics infrastructure and competencies as prerequisites of obtaining business value from data analytics. The final two papers deal with data analytics in the healthcare industry, a domain with a vast amount of data that lags behind other industries in deriving value from data analytics. [Extracted from the article]

This paper highlights a selection of core ideas articulated by Bertin and leveraged by many researchers over time, with particular attention to how the ideas relate to developments in cartography, big data, and visual analytics. A primary contribution is a bibliometric analysis of the impact of Bertin's Semiology of Graphics at its 50th anniversary. A briefer bibliometric assessment of Graphics and Graphic Information Processing impacts is also provided. The bibliometric analysis includes exploration of citations to Semiology of Graphics over the entire time span (in both English and French editions) as well as more focused analysis by topic and outlet since the advent of visual analytics as a research domain. Then, very recent research related to cartography, visual analytics, and big data is examined in detail to determine if and how Bertin's ideas continue to be leveraged and extended for current data representation and analysis challenges. After outlining some limitations of the bibliometric analysis, discussion reflects on the current relevance of Bertin's ideas, potential applications in visual analytics, and the need for a complement to Sémiologie Graphique focused on interactive visual interfaces to an increasingly diverse array of display forms. The paper concludes with thoughts on next steps. [ABSTRACT FROM AUTHOR]

Smart manufacturing that involves tight integration of the physical system and cyber system is a hot topic in both industry and academia in the era of the Internet and big data. However, the dynamic and uncertain manufacturing environment introduces a significant adaptive issue of production scheduling, which is one of the pivotal tasks for smart manufacturing. This paper focuses on this problem and proposes a closed-loop adaptive scheduling solution based on the Cyber-Physical Production System (CPPS) with four phases: production data acquisition (PDA), dynamic disturbance identification (DDI), scheduling strategy adjustment (SSA), and schedule scheme generation (SSG). In the DDI phase, in view of the disturbance classification, a disturbance identification procedure based on CPPS monitoring is studied to ensure real-time response. In the SSA phase, an industrial big-data-driven scheduling strategy adjustment method is proposed, which consists of GA-based offline knowledge learning and KNN-based online adjustment, to enhance the system adaptability. We apply and verify the proposed adaptive scheduling solution on an experimental semiconductor manufacturing system, and the results demonstrate that the proposed method outperforms the dynamic scheduling method in terms of multiple objectives under different disturbance levels. [ABSTRACT FROM AUTHOR]

This section invites authors to submit articles about big data and media management which will be reviewed for publishing.

The quality level in manufacturing processes increasingly concerns manufacturing firms, as they respond to pressures such as increasing complexity and variety of products, more complex value chains and shortened time-to-market. Quality management is becoming increasingly challenging as model variety, and highly complex products harbour the danger of distributing defective products in the market. Data analytics has started gathering the interest of quality researchers and practitioners, who investigate approaches, algorithms, and methods for supporting the manufacturing quality procedures in the context of Industry 4.0. This trend is facilitated by the wide expansion of sensory technology and the accelerated adoption of information systems by the manufacturing firms. Since quality and process control has been identified as one of the major challenges with a high potential of big data analytics, in this paper we investigated the manufacturing quality research field from a data analytics perspective. Specifically, we examined the existing literature, we provided clarity to the Quality 4.0 research field, we synthesized the literature review outcomes, and we identified the research gaps and challenges. On top of them, we proposed directions for future research. [ABSTRACT FROM AUTHOR]

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: "Tipping points and thresholds in hydrology," "Intensification of the water cycle," and "Water services under pressure." Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other early-career scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement. [ABSTRACT FROM AUTHOR]

Through big data analysis of borrowing information, it is possible to find the characteristics of different users. This paper applied K-means clustering to classify users. User feature vectors were extracted, and several algorithms were utilized to recommend books to different users. The hybrid algorithm achieved a precision of 96.29%, a recall rate of 92.13%, and an score of 94.16%, significantly higher than other algorithms. The findings prove that it is feasible to discover the characteristics of different users through big data analysis for book recommendation, and the hybrid algorithm performs the best. [ABSTRACT FROM AUTHOR]

A large body of academic research documents harmful media coverage of indigenous populations across the globe. New Zealand is no exception. Aotearoa's indigenous people, Māori, share similar damaging experiences, leading one major NZ media company to publish an apology for their historically poor depiction of tangata whenua. This paper adds to that wealth of evidence using the automated methods of machine learning to examine coverage of Māori in NZ print media. Across roughly 800,000 sentences – spanning over two decades of coverage – this research investigates print media discourses involving Māori at a mass scale while demonstrating the applicability of such tools for further research. The results replicate a collection of existing findings at large-N scale, further documenting problematic discussions of violence, political representation, and culture, among several other concerns. The novel approach also hints at complex, obscure relationships embedded within problematic language in Aotearoa's print media, identifying notions of division - both implied and otherwise - along with notable instances of resistance. [ABSTRACT FROM AUTHOR]

.Commercial platforms are being increasingly used in classrooms as teaching and learning tools. Beyond shifts in practice to accommodate this, the teacher becomes a tool of the platform – generating and collecting data for commercial entities. It is time to move from commercial platforms working through teachers to influence education, towards working with teachers in the ethical use of educational data. The purpose of this paper is twofold. Firstly, we draw on semi-structured interviews to present an exploratory typology of teacher influencer behaviour. Secondly, we argue that there is potential for teacher influencers to act as advocates. We introduce the conceptual notion of an 'educational data advocate'. We call for Teacher Influencers to be recognised as a source of expertise to understand the implications of the use of data-driven educational technologies. By doing so, a new pedagogical economy that promotes ethics and rights alongside educational and commercial outcomes may be generated. [ABSTRACT FROM AUTHOR]

This paper supports the personal data platform cooperative as a means of bringing about John Rawls's favoured institutional realisation of a just society, the property-owning democracy. It describes personal data platform cooperatives and applies Rawls's political philosophy to analyse the institutional forms of a just society in relation to the economic power deriving from aggregating personal data. It argues that a society involving a significant number of personal data platform cooperatives will be more suitable to realising Rawls's principle of fair equality of opportunity. [ABSTRACT FROM AUTHOR]

Food delivery service is under prosperous development in China, and the huge FDPWG (food delivery package waste generation) has brought unignorable environmental burdens. Thus, it's important to clarify the distribution and composition of FDPW (food delivery package waste). Choosing Dongguan as the study objective, this study developed an empowerment calculation method based on the POI (Point of interest) data, DHM (demographic heat map) data and FDPW data, to clarify the spatial patterns of FDPWG from food delivery merchants to consumers. The FDPWG results were aggregated into 1 km × 1 km grid, town, and functional area levels. Total FDPWG of Dongguan was 4796.68 t per month, composed of 67.61% plastic, 30.88% paper & wood, and 1.51% aluminum. Among plastic materials, HDPE (high density polyethylene), LDPE ((high-density polyethylene), PP (polypropylene), PS (polystyrene), PET (polyethylene glycol terephthalate), and the others took the share of 4.56%, 7.44%, 75.86%, 6.74%, 0.30%, and 5.12%, respectively. It illustrated that FDPWG had a positive correlation with population and GDP (Gross domestic production), and the influences of population and GDP were almost the same. With the level of local urbanization decreased, the plastic proportion had a decreasing trend, and the paper & wood proportion and aluminum proportion had an upward trend oppositely. Policy implications for FDPW management were raised based on the calculation results of FDPWG. [ABSTRACT FROM AUTHOR]

To date, research has mainly discussed the definition and classification of producer services. Yet many companies require an integrated solution of products and producer services. The aim of this paper is to propose a model of the ecological evolution of manufacturing service systems (MSS) driven by service providers. This model adopts modular thinking to integrate dispersive producer services into an entire MSS, the process of which forms the ecological evolution of MSS. This paper applies ecological theory to build the ecological evolution mechanism of MSS. In this mechanism, the predator-prey relationship is used as a metaphor for the relationships between manufacturers and service providers in a MSS. A predator-prey model is built to simulate the ecological evolution of MSS. A predation cellular genetic algorithm is adopted to optimise this evolution. The proposed model and algorithm are demonstrated and validated by a case study of MSS in bicycle industry. This study contributes to theory by proposing and clarifying the mechanism for provision of services between businesses. The future research will focus on the application and empirical analysis of this mechanism derived from big data. [ABSTRACT FROM AUTHOR]

In order to promote the development of agro-ecological economy and improve the reliability of the development model of agricultural circular economy, this paper explores the general framework of the coordinated development of the agricultural logistics ecosystem from the perspective of the construction of the agricultural logistics ecosystem, the composition of the ecosystem, the synergy stage, and the synergy goal. Moreover, this paper combines big data and Internet of Things technology to analyze its own path of the cooperative development mechanism of the agricultural logistics ecosystem, and uses typical cases to show the internal relationships between the main bodies of the agricultural logistics ecosystem. In addition, this paper attempts to explain the core essence of the agricultural logistics ecosystem and the internal relations of the subjects through the practice of enterprises. After constructing the system framework, this paper combines the intelligent algorithm to construct the intelligent model structure, and analyzes the system function realisation process based on the actual situation. Finally, this paper evaluates the performance of the algorithm model constructed in this paper through experimental research. From the experimental statistical results, it can be seen that the algorithm model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

In order to explore the intelligent path of agricultural sustainable development, this paper combines big data technology to process agricultural sustainable development data with the support of machine learning technology. Moreover, in order to solve the evaluation problem of regional agricultural sustainable development as well as understand the evolution path and evolution law of regional agricultural sustainable growth, this paper starts from the framework model and combines big data in addition to artificial intelligence expertise to construct a sustainable agricultural analysis model. In addition, this paper combines the needs of sustainable agricultural development to set the system function and analyse the realisation process of the system function. At last, this paper proposes a digital simulation experiment to authenticate the accomplishment of the model built in this paper. Through experimental research, it can be known that the intelligent agricultural sustainable development system constructed in this paper has certain effects, and this system can be used in subsequent research to analyse agricultural sustainable development. Based on algorithm verification as well as evaluation of the model on agricultural sustainability analysis, the statistical results are varying from 80 to 90. [ABSTRACT FROM AUTHOR]

This paper takes an interdisciplinary approach, combining legal studies, ethics and technical insights to shed light on the complex issues surrounding the regulation of predictive analytics. We argue that the individualised concept of regulation, shaped by the dogma of fundamental rights, is unable to adequately capture the implications of predictive analytics. We show that predictive analytics is a problem of collective privacy and informational power asymmetries, and conceptualise the form of data power at work in predictive analytics as 'prediction power'. The unregulated prediction power of certain actors poses societal risks, especially if this form of informational power asymmetry is not normatively represented. The article analyses this legal lacuna in the light of recent case law of the European Court of Justice and new legislation at the EU level. To address these challenges, we develop the concept of 'predictive privacy' as a protected good based on collective interests. [ABSTRACT FROM AUTHOR]

Uveitis is a heterogeneous collection of infrequent diseases, which poses significant challenges to cost-effective research in the field. Medical registries are being increasingly recognized as crucial tools to provide high-quality data, thus enabling prospective clinical research. This paper describes the design and technical structure development of an innovative countrywide electronic medical record for uveitis, Uveite.pt, and gives an overview of the cohort registered since its foundation, March 2020. Uveite.pt is an electronic medical record platform developed by the Portuguese Ocular Inflammation Group (POIG), a scientific committee of the Portuguese Ophthalmology Society. This is a nationwide customized web-based platform for uveitis patients useful for both clinical practice and real-world-based research, working as a central repository and reporting tool for uveitis. This paper describes the technical principles, the design and the development of a web-based interoperable registry for uveitis in Portugal and provides an overview of more than 400 patients registered in the first 18 months since inception. In infrequent diseases, the existence of registries enables to gather evidence and increase research possibilities to clinicians. The adoption of this platform enables standardization and improvement of clinical practice in uveitis. It is useful to apprehend the repercussion of medical and surgical treatments in uveitis and scleritis, supporting clinicians in the strict monitoring of drug adverse reactions and surgical outcomes. [ABSTRACT FROM AUTHOR]

This article examines the datafication of ageing by drawing on a practice approach toward care. We describe the datafication of ageing as a matter of care, achieved through the local tinkering of actors – technology designers, care staff, older adults, and highlighting the practices necessary to develop, maintain and implement data infrastructures. This paper draws on research conducted in a qualitative interview study in a LTC facility that uses AI-supported sensors to detect, predict and alarm care staff about falls of older residents. 18 interviews with developers, staff, residents and interest groups were conducted, as well as 24 h of participant observation in the care facility. The results reveal how AI-development for older target groups is characterized by absent data on these populations. Designers turn to practices that decontextualize data from the realities of older adults, relying on domain experts or synthetic data. This decontextualization of data requires recontextualization, with staff and older residents ensuring that the system functions smoothly, adapting their behavior, protecting the system from making false decisions and making existing care arrangements 'fit' the databases used to monitor activities in these arrangements. The ambivalent position of older adults in this data assemblage is further highlighted, as their caring practices are made invisible by different actors through ageist stereotypes, positioning them as being too frail to understand and engage with the system. While their bodily behavior is core for the databases, their perspective on and engagements with the operating system are marginalized, rendering some aspects of ageing hyper-visible, and others invisible. [ABSTRACT FROM AUTHOR]

Supply chain decarbonisation has become a strategic requirement in the era of a net-zero economy. Despite the significant role of Big Data Analytics (BDA) in decarbonising the supply chain (SC), no prior study has evaluated it systematically. The present study aims to provide a systematic literature review on the applications and outcomes of big data analytics in SC decarbonisation. A total of 69 papers on applying BDA technology for supply chain decarbonisation published between 2016 and 2021 have been selected following the PRISMA protocol. The findings show that the topic is evolving. Studies employed methods such as surveys (30), case studies (11), and conceptual research designs (8). Thematic analysis reveals that 65% of the studies are grounded in resource-advantage theories, organisational theories, and system theories. Studies from India and China (35%) dominate the topic, while most studies have been conducted on the food and manufacturing industries. Further, this study applied the Antecedent-Decision-Outcomes (ADO) framework in BDA-based SC decarbonisation. Antecedents include BDA resources and capabilities, workforce skills, and supplier capabilities. Decisions refer to improving decision-making across the supply chain. Outcomes refer to improving decarbonisation, sustainable growth, and sustainable innovativeness. Future research directions and questions are provided using the Theory-Context-Methodology (TCM) framework. [ABSTRACT FROM AUTHOR]

The importance of tourism expenditure is widely recognized, but the spatial patterns of visitor expenditure in cities is so far an unexplored subject. The main aim of this paper is to analyze the spatial distribution of visitor spending using bank card transaction data. Spatial statistics tools are used to identify expenditure clustering patterns and hot spots, distinguishing between national and foreign visitors, and comparing their patterns with those of locals. Our analysis shows that visitor expenditure, and especially that of foreigners, is much more concentrated in the city centre and spatially clustered than local spending. OLS regressions and spatial regression models are used for explaining the spatial distribution of visitor expenditure. Predictors are extracted from old (Cadastre) and new (the social network Foursquare) data sources. Regressions results indicate that socio-symbolic variables have greater explanatory power than physical dimension variables, that expenditure falls sharply with the distance to the city centre, and that the amount of spending in each spatial unit is influenced by the expenditure in the neighbouring spatial units. The paper demonstrates the great potential of this data source to explore with a high level of detail the spatial patterns and drivers of visitor spending within urban destinations. [ABSTRACT FROM AUTHOR]

In order to improve the effect of agricultural waste supervision and prevention and control of agricultural environmental pollution, under the concept of smart agriculture and circular economy, this paper builds a smart agricultural waste discharge supervision and prevention system based on big data technology. Moreover, this paper uses mathematical models such as the Environmental Kuznets (EKC) model and the decoupling model to study the overall scale of agricultural waste carbon emissions and its growth trend, and uses the logarithmic average Dixie Index method (LMDI) to construct a decomposition model of carbon emission driving factors to interpret the influence of each driving factor on the scale of overall agricultural waste discharge. In addition, this paper constructs the functional modules of the smart agricultural waste discharge supervision and prevention system according to actual needs, and conducts a test analysis of the system performance. The research results show that the system constructed in this paper has certain practical effects and can lay a foundation for the subsequent sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

Logistics 4.0 aims at enabling the sustainable satisfaction of customer demands with optimised costs of services with the use of emerging technologies, such as Internet of Things, streaming analytics, and optimised decision making. The availability of massive sensor data streams over time opens new perspectives for extracting meaningful and timely insights from data-in-motion through streaming analytics. Logistics 4.0 is a relatively new field of research which demands the development of scalable and efficient software solutions and their deployment to successful real-life case studies. In this paper, we propose a software framework for streaming analytics in an edge-cloud computational environment aiming at covering the whole data analytics lifecycle in logistics processes and thus, advancing the evolution and realisation of the Logistics 4.0 concept. The proposed framework takes advantage of edge computing technologies, streaming analytics and proactive decision making in order to monitor, analyse and support decision making in the frame of Logistics 4.0. It is applied and evaluated in a maintenance service logistics use case from the aerospace industry. [ABSTRACT FROM AUTHOR]

AI is a technological advancement used nowadays extensively by telecoms to take decisions based on the vast amounts of data they own and to optimise their daily operations. Hence, a question being raised amongst experts in this industry is how AI can be applicable in the various functions of telecoms. This paper showcases how the telecommunication industry could adopt Artificial Intelligence mechanisms into daily tasks and operations, in order to better utilise available data and accelerate digital transformation. The scope of this paper is to analyse and explore the opportunities and the challenges that have risen in telecommunications organisations, but more precisely, describes experiences from the IT Innovation Center of OTE Group that investigates and validates AI-related technologies in real business scenarios, aiming to boost and even further the organisation's digital transformation and engagement in future markets. However, as depicted in this paper, although the numerous opportunities, telecoms face many obstacles which they try to overcome in this AI journey. [ABSTRACT FROM AUTHOR]