Application Of Big Data Analytics Research Articles

DRG-based payment system and management accounting changes in an Indonesian public hospital: exploring potential roles of big data analytics

PurposeThis study aims to provide insights into management accounting changes (MACs) and potential roles of big data analytics (BDA) in accelerating the MACs in an Indonesian public hospital as a response towards the adoption of the diagnosis-related groups (DRG)-based payment system.Design/methodology/approachA mixed-method approach was used to collect and analyse data from a referral public hospital in Indonesia. First, a BDA simulation was carried out to reveal its usefulness in predicting and evaluating patient costs, and finally improving the cost recovery rate (CRR) of each DRG case. This part formulated and tested the mathematical models that predict patient cost, the CRR and determinants (length of stay/LOS, severity/SEV, patient age/AGE and gender/SEX). For this purpose, data of the top ten inpatient cases of 2018 were collected and analysed. Second, semi-structured interviews with senior staff and doctors were carried out to understand cost control strategies implemented in the hospital and the management and doctors’ perceptions regarding the application of tested mathematical models for cost control. Old institutional economics and new institutional sociology were used to gain insight about how and why management accounting practices changed in the hospital.FindingsThe findings show that the absence of detailed per-case/patient cost information has not only hindered further evolvement of MACs but also stimulate tensions between managerial and medical worlds in the studied Indonesian public hospital. The simulation of BDA in this study was not only discovering the determinants of case cost recovery but also enabling the prediction of CRR of patients immediately after admission. The application of BDA and casemix accounting in the hospital will potentially become catalysts of discussion and mutual learning between managerial and medical staff in controlling patient costs.Originality/valueThis paper provides a more comprehensive picture of the potential roles of BDA in cost control practices. The study assesses the feasibility of BDA application in the hospital and evaluates the potential roles and acceptance of BDA application by both management and doctors.

Application of Big Data Analysis in Medical and Health Institutions in Chengdu

With the development of social economy, information technology is developing constantly, and the whole society has entered the era of big data. The era of big data is mainly manifested in the large amount of data, various types, low value density, and high requirements for data processing speed and timeliness. In the era of big data, we should extract data in time and effectively, so as to promote the development of various industries. As an institutional industry that plays an important role in the cure of human health diseases, medical and health institutions have begun to gradually use big data to play an important role in the management of people's diagnosis and treatment. Taking medical and health institutions in Chengdu as an example, this paper explores how to use big data analysis in medical and health institutions, so as to improve the efficiency of medical treatment and medical quality.

Tackling Delinquency in the Era of Social Credit System: Prospects and Risks

The paper is devoted to the changes in fighting delinquency connected to the application of artificial intelligence and Big Data analytics. The focus of the paper has been made on the Social Credit System and related advanced mechanisms of control and surveillance, which are currently being built and implemented in China. The issue of how the latest technologies of social control impact the fight against crimes and administrative offences has been examined. The transforming effect of introduction of the Social Credit System and algorithmic mechanisms of social control upon the legal system and some of its institutions (notably, the legal liability institution, the punishment, the concept of an offender) has been assessed in the paper. The authors come to the conclusion that the introduction of the Social Credit System in China and the development of algorithmic mechanisms of social control and crime prevention may lead to the separation of punishment from the construct of legal liability and the concept of an offence as a guilty deed.

Risks in Adoption and Implementation of Big Data Analytics

The research investigates the risks in adopting and implementing big data analytics in Indian micro, small, and medium enterprises (MSMEs). The researchers outlined a survey questionnaire for accumulating reactions from managers working in 50 Indian micro, small, and medium-sized enterprises on behalf of five vital commercial sectors. The application and use of big data analytics offer several significant problems for small companies as an investment in hardware and software resources are substantial. This study's findings provided experimental evidence on five critical challenges that Indian MSMEs face while adopting and implementing big data analytics: lack of human resources, data privacy and security, shortage of technological resources, deficiency of awareness, and financial implications. This study's findings emphasize the challenges that MSMEs face while leveraging big data analytics benefits. The research outcome will promote MSMEs' organizational leadership in planning and developing short-term and long-term information systems strategies.

The Value of Big Data Analytics Pillars in Telecommunication Industry

In the Big Data age, businesses in every industry must deal with vast volumes of data. Several experts and practitioners have lately emphasized the need of understanding how, why, and when Big Data Analytics (BDA) applications may be a valuable resource for businesses seeking a competitive edge. However, BDA pays off for some firms while failing to pay off for others due to the fact that investment in Big Data continues to present significant challenges due to the missing link between analytics capabilities and firm performance. According to a recent survey, many businesses spend the bulk of their time analyzing data, with only a tiny fraction employing Big Data Analytics to forecast outcomes and even fewer utilizing analytics apps to enhance processes and strategies. As a result, BDA is not widely used, and only a few companies have seen any benefit from it. To address this issue in the telecommunications domain and in light of the paucity of research on the subject, this study focused on the BDA Pillars (BDAP) in order to achieve benefits through increased revenues and cost savings. For the purpose of this research we have adopted qualitative approach with case study method, and technique of data collection includes semi-structure interview and document analysis. The Delphi technique and in-depth interviews conducted confirmed the existence of five critical elements that contribute to the sustainability of BDAPs and their impact on firm performance.

Applications of Big Data Analytics in Healthcare Management Systems

The healthcare industry has become increasingly demanding in recent years. The growing number of patients makes it difficult for doctors and staff to manage their work effectively. In order to achieve their objectives, data analysts collect a large amount of data, analyze it, and use it to derive valuable insights. Data analytics may become a promising solution as healthcare industry demands increase. The paper discusses the challenges of data analytics in the healthcare sector and the benefits of using big data for healthcare analytics. Aside from focusing on the opportunities that big data analytics has in the healthcare sector, the paper will also discuss data governance, strategy formulation, and improvements to IT infrastructure. Implementation techniques include Hadoop, HDFS, MapReduce, and Apache in Big Data Analytics. A Healthcare Management System can be categorized into five divisions, namely, Drug discovery, Disease prevention, diagnosis and treatment, Hospital operations, post-care, requiring comprehensive data management. Big Data analysis support transformation is identified as a required component in future research for the application of Big Data in HealthCare.

Big data analytics for intelligent manufacturing systems: A review

With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the amount of data from manufacturing systems has been increasing rapidly. With massive industrial data, achievements beyond expectations have been made in the product design, manufacturing, and maintain process. Big data analytics (BDA) have been a core technology to empower intelligent manufacturing systems. In order to fully report BDA for intelligent manufacturing systems, this paper provides a comprehensive review of associated topics such as the concept of big data, model driven and data driven methodologies. The framework, development, key technologies, and applications of BDA for intelligent manufacturing systems are discussed. The challenges and opportunities for future research are highlighted. Through this work, it is hoped to spark new ideas in the effort to realize the BDA for intelligent manufacturing systems.

Performance evaluation and analysis of distributed multi-agent optimization algorithms with sparsified directed communication

There has been significant interest in distributed optimization algorithms, motivated by applications in Big Data analytics, smart grid, vehicle networks, etc. While there have been extensive theory and theoretical advances, a proportionally small body of scientific literature focuses on numerical evaluation of the proposed methods in actual practical, parallel programming environments. This paper considers a general algorithmic framework of first and second order methods with sparsified communications and computations across worker nodes. The considered framework subsumes several existing methods. In addition, a novel method that utilizes unidirectional sparsified communications is introduced and theoretical convergence analysis is also provided. Namely, we prove R-linear convergence in the expected norm. A thorough empirical evaluation of the methods using Message Passing Interface (MPI) on a High Performance Computing (HPC) cluster is carried out and several useful insights and guidelines on the performance of algorithms and inherent communication-computational trade-offs in a realistic setting are derived.

Большие данные как ключевые мощности в антимонопольном регулировании цифровых рынков

The article is devoted to the issue of applying the Essential Facilities Doctrine to big data for the purpose of antitrust regulation of digital markets. The concept of big data is revealed, which currently does not have a normative legal fixation in the legislation of the Russian Federation. The article identifies the difference between big data and regular data, between the organizational, managerial, economic and legal content of these phenomena of the digital age. Based on the analysis of scientific publications, law enforcement and judicial practice of antimonopoly agencies of foreign countries and Russia, the author systematizes the directions of using big data in competition in digital markets. The article also reveals the content of the doctrine of essential facilities in competition law. The author describes the key ideas of the scientific discussion on the application of the doctrine of essential facilities to big data. The article contains author’s suggestions and recommendations for the correct division of the complex of big data technology into competitive (AI) and infrastructure (data sets) components. If necessary, the doctrine of essential facilities can be extended to computing power, server data storage, data sets (in an unstructured form) of digital giants. The author believes that applying the doctrine of essential facilities to big data can eliminate the abuse of dominance by tech giants. But the application of the doctrine of essential facilities is not able to systematically change the state of affairs in digital markets. In order to remove the incentives of Internet giants to abuse dominance with the help of big data, it is necessary to separate from the Internet giants business units, that would exclusively provide non-discriminatory access to Big Data management services. And the competencies associated with the development and application of big data analytics (AI) technologies should be assigned to business units, that would compete in derivative digital markets independently.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

ENHANCING THE COMPETITIVENESS OF BUSINESS AND MANAGEMENT CONSULTING FIRMS THROUGH THE APPLICATION OF BIG DATA AND ANALYTICS

This study investigates the application of big data analytics (BDA) and its impact on consulting firms’ competitiveness. Descriptive statistics, ordinary least square regression, and moderated regression analysis were used to analyze survey data obtained from 118 business and management consultants in Nigeria. The robustness of the result was evaluated using structural equation modeling. Result shows that the application level of BDA by consulting firms is generally moderate. BDA application has a significant positive impact on organizational competitiveness, although the strength of the relationship is weak. Further, data quality significantly moderates the relationship between BDA and organizational competitiveness. The study concludes that the application of BDA can enhance the competitiveness of consulting firms. However, the extent to which such benefit is realized is dependent on the quality of data applied in the analysis. The study contributes to knowledge by providing empirical evidence that the deployment of BDA can be a source of competitive advantage for consulting firms. The study adds to literature on management accounting in the digital economy and the application of big data to business and management consulting.

Drivers of implementing Big Data Analytics in food supply chains for transition to a circular economy and sustainable operations management

Purpose The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM). Design/methodology/approach Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia. Findings The results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM. Research limitations/implications The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics. Originality/value The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.

Application of Information Technology in Human Resource Management

With the increasingly fierce competition for talents, the continuous development of information technology, and the penetration of big data into various industries, human resource management has to think about the changes in information technology. It is necessary to explore the application of big data in human resource management to carry out more comprehensive and scientific human analysis, therefore it is necessary to explore the application of information technology in human resource management. In this context, this article discusses the combined application of big data analysis and human resource management, in order to provide some reference for the development of enterprise human resource management.

Influence and Application of Big Data Analysis in Product Design Research

Design research is an important work in product design. Through a series of data collection and data analysis, we can better adjust the design scheme. The traditional product design research mode data analysis effect is not good, through the Big Data(BD) technology research and analysis of product design, is a good solution at present. Therefore, this paper establishes the research on the influence and application of BD analysis in product design research. In this paper, the product design research and the application of BD analysis technology in the design research are deeply studied and analyzed. It is believed that through the BD technology, the research content can be analyzed more scientifically, so as to improve the overall product design effect. In order to make the BD analysis technology better applied in product design research, this paper optimizes and improves the evaluation crowd weight calculation method of entropy processing, which effectively improves the accuracy of the algorithm. In order to further verify the application effect of BD analysis in product design research; this paper establishes a test experiment with “mobile power supply” as the product. The experimental results show that among the tested users, young people aged 15-20 are the main ones, accounting for 38.7%. According to the analysis of users’ interests and hobbies, the highest proportion of digital options is 31.2%. The analysis shows that the results of BD analysis provide valuable reference for the formulation and adjustment of product design strategy.

Applications of Big Data Analytics to Control COVID-19 Pandemic.

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve competitive advantage and sustainable growth. Previous BDA research, however, has focused more on introducing more traits, known as Vs for big data traits, while ignoring the quality of data when examining the application of BDA. Therefore, this study aims to explore the effect of big data traits and data quality dimensions on BDA application. This study has formulated 10 hypotheses that comprised of the relationships of big data traits, accuracy, believability, completeness, timeliness, ease of operation, and BDA application constructs. This study conducted a survey using a questionnaire as a data collection instrument. Then, the partial least squares structural equation modelling technique was used to analyse the hypothesised relationships between the constructs. The findings revealed that big data traits can significantly affect all constructs for data quality dimensions and that the ease of operation construct has a significant effect on BDA application. This study contributes to the literature by bringing new insights to the field of BDA and may serve as a guideline for future researchers and practitioners when studying BDA application.

Assessment of the Awareness of Nigerian Professionals in the Built Environment on the Big Data analytics (BDA) Applications in the Construction Industry.

With rapid development in technology, the built industry’s capacity to generate large-scale data is not in doubt. This trend of data upsurge labelled “Big Data” is currently being used to seek intelligent solutions in many industries including construction. As a result of this, the appeal to embrace Big Data Analytics has also gained wide advocacy globally. However, the general knowledge of Nigeria’s built environment professionals on Big Data Analytics is still limited and this gap continues to account for the slow pace of adoption of digital technologies like Big Data Analytics and the value it projects. This study set out to assess the level of awareness and knowledge of professionals within the Nigerian built environment with a view to promoting the adoption of Big Data Analytics for improved productivity. To achieve this aim, a structured questionnaire survey was carried out among a total of 283 professionals drawn from 9 disciplines within the built environment in the Federal Capital Territory, Abuja. The findings revealed that: a) a low knowledge level of Big Data exists among professionals, b) knowledge among professional and the level of Big Data Analytics application have strong relationship c) professional are interested in knowing more about the Big Data concept and how Big Data Analytics can be leveraged upon. The study, therefore recommends an urgent paradigm shift towards digitisation to fully embrace and adopt Big Data Analytics and enjoin stakeholders to promote collaborative schemes among practice-based professionals and the academia in seeking intelligent and smart solutions to construction-related problems.

WITHDRAWN: Data science based analytics of real time data and key dimensions with emotions evaluations

Abstract The approach of analysing massive data types, that are big data, is the analysis of hidden trends, unexplained interactions, market conditions, customer preferences and other important business information. The analysis is focused on big data analysis. The empirical findings would continue to maximise the performance and productivity of marketing, new sales perspectives, better service for consumers and improved organisational performance. The key goal of big data analysis is to help companies select best by empowering data scientists, statistical modellers and other researchers to assess large quantities of transaction data and Other sources of data which traditional business intelligence systems could not take advantage of. These include logs of webservers and clickstream data, social media material and social network notifications, user e-mail text and survey ansverses, telephone call reports and device data from sensors connected to the Internet of Things. Few may only compare Big data on semi-organised and unstructured information such as this, but also analyst companies such as Gartner Inc. and Forrester Research Inc. intrinsics to Big-Data analytics applications of transactions and other structured data.

Differentially private locality sensitive hashing based federated recommender system

SummaryRecommender systems are important applications in big data analytics because accurate recommendation items or high‐valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine‐grained data for training. In the current big data era, data often exists in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy‐preserving recommender system is of paramount importance. Existing privacy‐preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we conduct privacy analysis on the existing locality sensitive hashing (LSH) approach based privacy‐preserving recommender system and show how an attacker can retrieve user's information under such a recommender system. Given such privacy risks, we propose differentially private LSH approach to build recommender system that can offer differential privacy guarantees for users. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing privacy preservation of users' data in contributing parties. Extensive experiments on real‐world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets.

Big data analytics and competitive advantage: the strategic role of firm-specific knowledge

PurposeWhile advances in big data analytics (BDA) provide valuable business insights and immense business value, many firms find it difficult to gain advantage from their BDA initiatives. Noting the strategic role of firm-specific knowledge, we develop a framework examining the relation between firm specificity of BDA knowledge and competitive advantage. We also examine the dynamic evolution of BDA capabilities and the associated knowledge management strategies.Design/methodology/approachWe review the resource-based view (RBV), capabilities life cycles and absorptive capacity perspectives along with the literature on BDA competitive advantage. Identifying two key BDA factors, application customization and data proprietorship, we develop a BDA competitive advantage framework. We also investigate the absorptive capacities employed by firms to advance their BDA capabilities. We use anecdotal cases to support our theoretical arguments.FindingsWe propose that BDA solutions with vendor-based applications (noncustomized) and public data will not generate firm-specific knowledge and therefore not provide competitive advantage. In contrast, BDA solutions with custom applications and proprietary data will provide high-level firm-specific knowledge and potentially result in sustained competitive advantage. We further suggest the relevant absorptive capacities and the knowledge management strategies for BDA capability development.Practical implicationsOur framework provides managers with insights into how to develop and enhance firm-specific knowledge from their BDA solutions to gain competitive advantage.Originality/valueOur study offers a new BDA firm-specific knowledge framework for competitive advantage.