Big Data Practices Research Articles

The Use of Big Data in Corporate Accounting and Data Analysis: Opportunities and Challenges

IntroductionThe era of Big Data technologies is restructuring corporate accounting, enabling a wide array of dynamic potential. This project explores how Big Data affects financial management, focusing on forecasting, risk management, and technological advances.MethodsThis work is informed by a large-scale review of scholarly literature, industry reports, and case studies. Databases like Google Scholar, PubMed, IEEE Xplore, Scopus, and Web of Science were used for data collection. Keywords included Big Data, corporate accounting, financial forecasting, risk management, data analytics, AI in accounting, machine learning in finance, and blockchain technology applied to accounting. The review was structured thematically, focusing on financial forecasting, risk management, and ethical considerations affected by Big Data practices in this domain.ResultsBig Data improves financial forecasting accuracy using historical data, market trends, and consumer behavior analytics. In risk management, Big Data facilitates effective proactive actions through thorough risk evaluation. Emerging technologies are anticipated to automate complex tasks, enhance predictive analytics, and improve the security and reliability of financial transactions.ConclusionsBig Data holds significant potential for corporate accounting, though challenges such as managerial complexity, data privacy, and expertise requirements for handling large volumes of data remain. The study highlights the importance of flexibility and technological adaptability, as well as specialized skill sets. It calls for continual dialogue and policy development to meet the ethical challenges presented by Big Data/AI, promoting responsible deployment while ensuring fairness. This review contributes to academic discourse and provides strategic guidance for practitioners in the evolving landscape of corporate accounting

A Study on the Comprehensive Education Model of "Job Course Competition Certificate" for Vocational Undergraduate Big Data and Accounting Majors

The comprehensive education model of "on-the-job course competition certification" is an innovative talent cultivation model aimed at cultivating high-quality skilled talents that meet the needs of the new era. This model combines job skills, course learning, skill competitions, and certificate certification to provide students with a more practical educational experience. This time, the comprehensive education model of "on-the-job course competition certificate" will be applied to the teaching practice of vocational undergraduate big data and accounting majors. After briefly explaining the connotation of the "on-the-job course competition certificate" education model, this article analyzes and summarizes the integration methods of "on-the-job course competition certificate" from four aspects: course post integration, course certificate integration, course competition integration, and competition certificate integration. Finally, combining with the current situation of big data and accounting major teaching in vocational colleges, this article explores the practical path of the "on-the-job course competition certificate" comprehensive education model from the rational allocation of school enterprise resources, setting of talent training standards, construction of teacher teams, optimization of curriculum systems, and improvement of assessment and evaluation systems.

Big Data Analytics Implementation and Practices in Medical Institute Libraries of Pakistan

Abstract The aim of this research was to analyze the contemporary practices of Big Data Analytics (BDA) in medical libraries of Pakistan and to explore ways for its implementation in these libraries. A cross-sectional study was carried out among medical librarians in Pakistan by using a Modified Technology Acceptance Model (MTAM) model, with collected data analyzed using Smart-PLS. The results revealed that lack of resources, technical expertise, and some other factors were significant hurdles in smooth implementation of BDA. It is concluded that the perception of medical librarians about adoption of BDA is very encouraging, which resultantly will improve the quality of healthcare services in the country.

СПЕЦИФИКА ПРОИЗВОДСТВА И ИСПОЛЬЗОВАНИЯ БОЛЬШИХ ДАННЫХ В ГОСУДАРСТВЕННОМ УПРАВЛЕНИИ

Differences in the practice of Big Data (BD) usage between the private and public sectors are causing a problem of public administration access to advanced BD technologies and methods used in business. The high potential of BD is in demand by public organizations while private businesses hold these technologies and methods. The study of barriers to the dissemination of a practice of BD usage in public administration allows identification of possibilities and directions for BD production and use by public organizations. Russian and foreign cases of production and incorporation of BD in public administration for their analysis were selected from scientific and business sources. The barriers limiting work with BD in public administration are identified: a legal regulation of BD implementation, the structure and functioning of the IT infrastructure of public administration, requirements for the quality of data as content, and the scope and scale of socio-political development problems in public administration. Analysis of BD application in public administration has shown the ways of BD production, such as signal reception and date entry, as well as the necessity of using the IT infrastructure of business. Ways of implementing BD vary due to their multitasking in the public and private sectors. Since BD accumulated by public organizations is in demand by private businesses, providing a data access mechanism can be an approach to enhancing the digital economy at the local and state levels. The study demonstrates the high importance of finding the ways to develop and disseminate BD for public authorities.

Hanyang Practice of Big Data Enabling Supervision of Administrative Violations

Hanyang Practice of Big Data Enabling Supervision of Administrative Violations

Acute Kidney Disease in Outpatient Setting: Epidemiological Insights from National Digitalized Big Data Practice

Acute Kidney Disease in Outpatient Setting: Epidemiological Insights from National Digitalized Big Data Practice

The Open Prison of the Big Data Revolution: False Consciousness, Faustian Bargains, and Digital Entrapment

Although some scholars raise alarm about societal harm emerging from Big Data practices, critical social theory (CST) Information Systems research on the structures and dynamics driving Big Data practices is rare. In this research commentary, we interrogate how tech firms use social practices and platform design to strategically manipulate individuals into accepting datafication and data assetization that accrue positive data network effects for themselves and mostly negative data network effects (economic loss, social and privacy harm) for individuals. We draw on the ideas of Heidegger and Marcuse to critically question the Big Data paradigm in order to develop better understanding of the social implications for individuals and society. Using the concepts of false consciousness, digital entrapment, and Faustian bargains, we critically inquire into the Big Data practices that keep us tethered to digital platforms. Specifically, we interrogate sociomaterial structures that socially condition individuals into a digital habitus and to identify themselves as homo digitalis, who view all their “relations” (social and economic) as digital. This social conditioning reproduces a false consciousness that constricts our worldview, undermines our rational choices, and enables the risky compromises we make with tech companies that manipulate and exploit us with their increasingly oppressive Big Data practices and related dark patterns. We critically analyze the case of Microsoft Viva to provide an illustration of how mundane digital tools can condition our reality and entrap us into an open prison. We argue that if we do not critically interrogate our false consciousness of the digital and understand how digital giants colonize our social systems by structurally embedding Big Data practices, we will continue to be susceptible to manipulation and digital entrapment. Ongoing risky compromises with tech firms will erode the very foundations of the “good life,” freedom, liberty, and personal privacy, and they will institutionalize the open prison. The CST explanation we propose and the research agenda we outline are meant to encourage research into solutions to the digital entrapment problem.History: Suprateek Sarker, Senior Editor; Robert Gregory, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2020.0588 .

The Construction and Practice of Big Data Mapping GIS under Low Altitude Remote Sensing

This paper introduces the construction and practice of big data mapping geographic information system under low-altitude remote sensing. The combination of low-altitude remote sensing technology, which obtains high-resolution remote sensing data through unmanned aerial vehicles and satellites, and big data technology, which can process and analyze huge geographic information data, provides new ideas and methods for the development of geographic information systems. Starting from an overview of low-altitude remote sensing technology and the advantages of its application, the article discusses the process of building a big data mapping GIS and introduces a practical case based on the system. At the same time, the current challenges and future development trends are also put forward.

Governance Democratic and Big Data: A Systematic Mapping Review

The use and management of Big Data in the political sphere has posed unprecedented challenges concerning democratic governance, equity, and the rule of law. As Big Data establishes itself as a resource of growing value, it is imperative to address one of the most critical challenges: data sustainability. Data sustainability involves social and ethical considerations relating to the correct use of personal data. Lack of informed consent and transparency in collecting and using personal data raises serious concerns regarding privacy and individual rights. It is necessary to define regulations and public policies that guarantee citizens’ digital rights based on ethical and democratic standards associated with data management. This article aims to review the literature in the context of data sustainability to identify how Big Data is used, particularly emphasizing its application in the policy domain and the challenges it poses for democratic governance, equity, and law. We have used systematic mapping methodology to collect relevant papers, finding 28 papers associated with democratic governance and Big Data in the context of data sustainability. From the review of these papers, there appears to be a lack of proposals focusing on applying or implementing democratic governance and Big Data. Furthermore, there seem to be no measures to assess the application of Big Data in democratic governance. From these, the need to move towards the definition of formal models that integrate Big Data practices in democratic governance is identified.

When data became big: revisiting the rise of an obsolete keyword

ABSTRACT This article unpacks the short-lived but momentous buzz around big data. Although talk about big data was once widespread, little is known about the efforts animating its semantics. Tracing this sociotechnical imaginary, we revisit how business insiders and IT commentators fueled the ephemeral yet potent excitement around the term. Our genealogical examination rests on a selection of publications from 2013 to 2017. We employ methods from critical discourse analysis to interrogate how big data was written into being and hyped into a topic of concern. In this aspirational discourse, tech evangelists and writers extrapolated from contexts in which large troves of data were already being harnessed to suggest that inescapable transformations were imminent. They sought to concretize abstract and unfathomable quantities while simultaneously overwhelming their readers with a sense of vastness that exceeds all contexts and outruns the most exuberant expectations. The term may have lost this luster, but big data technologies and practices are an integral part of today’s technological infrastructures.

System analysis of big data for Earth sciences

Currently, big data is one of the most discussed phenomena in the field of information technology. What is big data? How are they created? How does big data software work? How is big data theory and practice used and can be used in relation to geosciences? How is the issue of big data developing within the Russian Academy of Sciences? These are the questions discussed in an article prepared on the basis of a scientific report presented at a meeting of the Presidium of the Russian Academy of Sciences on March 28, 2023.

Analisis Praktik Audit Big Data Environtment di Indonesia

This study aims to determine auditing in the Big Data of Environment which is combined with the status of research on big data auditing at home and abroad and based on the characteristics of the big data environment. The method used in this study is descriptive qualitative with a literature review. The results of this study are as the newest product of innovation and integration of a new generation of information technology, big data has had a significant impact on various fields. This research proposes logical workflow and technical methods of data-driven auditing and proposes suggestions on building big data audit platforms, information sharing, talent training, and data security, providing ideas for research and practice of big data auditing modes.

A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences

The use of Artificial Intelligence (AI) and Big Data analysis algorithms is complementary to theory-driven analysis approaches and becoming more popular also in social sciences. This paper describes the use of Big Data and computational approaches in social sciences by bibliometric analyses of articles indexed between 2015 and 2020 in Social Sciences Citation Index (SSCI) of the Web of Science repository. We have analysed especially the recent research direction called Computational Social Sciences (CSS) that bridges computer analytical approaches with social science challenges, generating new methodologies of Big Data and AI analytics for social sciences. The results indicate that AI and Big Data practices are not confined to CSS only and are diffused in a wide variety of disciplines under Social Sciences and are made use of in many main research lines as well. Thus, the anticipated overlap between the Social Sciences & AI specialization and CSS has yet to be crystallised. Moreover, the impact of computational social science studies is not permeated to social science citation networks yet. Lastly, we demonstrate that the AI and Big Data publications that appear under the SSCI index are more oriented towards computational studies than addressing social science concepts, concerns, and challenges.

Analysis of AI based Data Wrangling Methods in Intelligent Knowledge Lakes

A novel conception of Knowledge Lake, i.e., a Contextualized Data Lake is to be soundly educated. The deliberated big-data practices pave a means for the erection of Intelligent Knowledge Lakes and that being the resources for big-data applications and analytics. This analysis likewise opens the welfares, disputes, and exploration prospects of Intelligent Knowledge Lakes. Data Science is launched as an influential discernment through businesses. Organizations today are dedicated on transforming their facts into ultra-practical intuitions. This work is challenging, as in present day’s intelligence, amenity and cloud customary budget trades accumulate immense aggregates of unprocessed data after a variety of funds. Data Lakes are familiar as a packing depository that fetch concurrently the unprocessed data in its innate set-up (sustaining to NoSQL from relational databases) which is crucial. The logic behind Data Lake is to stockpile unprocessed data and let the data analyst resolve the way to curate them well ahead of reviewing the idea of Knowledge Lake, which is an anecdotal Data Lake. The Intelligent Knowledge Lake stipulate the basis for big data analytics by robotically curating the unprocessed data in the Data Lake grooming these for stemming intuitions via programmed interactive real-time optimized data wrangling in intelligent knowledge lakes. Computerization of an exposed free public Data and Knowledge Lake amenity provides developers and researchers a distinct REST API to systematize, curate, catalog and interrogate their data and metadata in the Lake for a longer time. It administers manifold database/databank know-hows (from Relational to NoSQL) that deals with an inherent scheme for data security, curation, and provenance.

Research on the Construction of University Campus Economic Management System Based on the Concept of Big Data

At present, in order to adapt to the development of the era of big data, more and more industries use the concept of big data, and the economic and financial management system is no exception. In the context of the concept of big data, the campus economy has also ushered in new opportunities and challenges. Combined with the basic knowledge of big data, this paper makes a systematic research on industrial economic information analysis, which will contribute to the application practice of industrial big data. Using the value of big data will promote revolutionary changes in the work of the school’s economic management department. Taking this as the starting point, this paper systematically analyzes the analysis methods of campus economic information under the background of big data and its role in macro decision-making.

Impact of big data analytics on supply chain performance: an analysis of influencing factors

This paper aims to understand the impact of big data analytics on the retail supply chain. For doing so, we set our context to select the best big data practices amongst the available alternatives based on retail supply chain performance. We have applied TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) for the selection of the best big data analytics tools among the identified nine practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on seven supply chain performance criteria (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). One of the intriguing understandings from this paper is that most of the retail firms are in a dilemma between customer loyalty and cost while implementing the big data practices in their organization. This study analyses the dominance of the big data practices at the retail supply chain level. This helps the newly emerging retail firms in evaluating the best big data practice based on the importance and dominance of supply chain performance measures.

Research on the Impact and Utility of Rural Revitalization Big Data Service on Farmers Based on Integrated Technology Acceptance Model

The integrated technology acceptance model was proposed in 2003 and has been gradually applied in many fields since then. At present, the promotion of agricultural big data has achieved initial results, and a number of agricultural big data practice demonstration projects have been formed. For example, Shandong Province supports the establishment of the “Big Data Platform of Bohai Granary Science and Technology Demonstration Project,” which includes four modules of data collection, mining and analysis, monitoring and early warning, and decision-making service. It is characterized by the diversity of massive data sources, the integration of historical and real-time data, and multifactor comprehensive analysis and decision making. However, farmers, as an important service subject of agricultural big data platform, do not have strong willingness to participate. In the era of big data, it is still difficult for farmers to obtain data information and services, and their ability to obtain, screen, and use data is poor. Therefore, it is of great practical significance to effectively improve farmers’ willingness to adopt and use agricultural big data. Based on the integrated technology acceptance model, this study analyzed the impact of big data service of rural revitalization on farmers. Firstly, the basic content of integration technology acceptance theory was analyzed, and the research hypothesis was proposed based on this theory. After that, the questionnaire survey was used to empirically analyze the impact of big data service on farmers. Reliability and validity test, correlation test, goodness of fit test, and path system test were carried out on the analysis results to prove the factors that affect farmers’ behavior and attitude of using big data.

Inauguration of a unique journal the Journal of Digital Health: a new beginning seeking innovative technology and research for digital health

As the Editor-in-Chief of the newly founded journal the Journal of Digital Health (JDH), I am honored to introduce this journal to you on behalf of Luminescience Press Ltd. JDH is an open access and peer-reviewed journal that publishes high-quality original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI), big data and informatics in the medical and healthcare industries to improve the quality and efficiency of healthcare. The journal publishes original articles, reviews, perspectives, research highlights and case reports that present the application of digital technologies in medical diagnostics and treatment, medical devices, machine learning-based decision support, medical record database and intelligent and process-aware information system in healthcare and medicine. We are committed to promoting the interdisciplinary research among clinicians, basic scientists and industrial experts and facilitating the clinical transformation of industrial technologies. Our ultimate mission is to enable the frontiers of science and technology better serve medicine and ultimately contribute to the healthcare of patients.

Inauguration of a unique journal the Journal of Digital Health: a new beginning seeking innovative technology and research for digital health

As the Editor-in-Chief of the newly founded journal the Journal of Digital Health (JDH), I am honored to introduce this journal to you on behalf of Luminescience Press Ltd. JDH is an open access and peer-reviewed journal that publishes high-quality original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI), big data and informatics in the medical and healthcare industries to improve the quality and efficiency of healthcare. The journal publishes original articles, reviews, perspectives, research highlights and case reports that present the application of digital technologies in medical diagnostics and treatment, medical devices, machine learning-based decision support, medical record database and intelligent and process-aware information system in healthcare and medicine. We are committed to promoting the interdisciplinary research among clinicians, basic scientists and industrial experts and facilitating the clinical transformation of industrial technologies. Our ultimate mission is to enable the frontiers of science and technology better serve medicine and ultimately contribute to the healthcare of patients.

AI, big data, and the future of consent.

In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a ‘soft governance’ ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts.