Big Data Predictive Analytics Research Articles

Big data and predictive analytics: A systematic review of applications

Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled.

The Impact of Big Data Predictive Analytics on Firm Performance: The Role of Cloud ERP and Business Intelligence Integration

The Impact of Big Data Predictive Analytics on Firm Performance: The Role of Cloud ERP and Business Intelligence Integration

Roles of top management support and compatibility in big data predictive analytics for supply chain collaboration and supply chain performance

With the global digitalisation, big data has received growing attention from academicians and practitioners. However, only a few empirical studies examined the benefits of big data predictive analytics (BDPA) and its influence on supply chain collaboration (SCC) and supply chain performance (SCP). Addressing the identified gaps of the implementation of organisational information processing theory (OIPT), the current study provided the foundation to develop a conceptual framework. All relevant data were collected from 197 employees in the Chinese logistics industry. Partial least squares–structural equation modelling technique was performed. The obtained empirical results supported top management support and compatibility as critical factors for the adoption of BDPA. Moreover, BDPA exhibited positive influence on SCC and SCP. Additionally, SCC mediated the relationship between BDPA and SCP. This study presented significant theoretical contributions and provided guidelines that can benefit policymakers and organisations in the efforts of implementing BDPA for enhanced SCP. After all, improving SCP would benefit customers and the society in the case of reduction and wastage of resources.

European Nephrologists’ Attitudes toward the Application of Artificial Intelligence in Clinical Practice: A Comprehensive Survey

Introduction: The rapid advancement of artificial intelligence and big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, has the potential to revolutionize many areas of medicine, including nephrology and dialysis. Artificial intelligence and big data analytics can be used to analyze large amounts of patient medical records, including laboratory results and imaging studies, to improve the accuracy of diagnosis, enhance early detection, identify patterns and trends, and personalize treatment plans for patients with kidney disease. Additionally, artificial intelligence and big data analytics can be used to identify patients’ treatment who are not receiving adequate care, highlighting care inefficiencies in the dialysis provider, optimizing patient outcomes, reducing healthcare costs, and consequently creating values for all the involved stakeholders. Objectives: We present the results of a comprehensive survey aimed at exploring the attitudes of European physicians from eight countries working within a major hemodialysis network (Fresenius Medical Care NephroCare) toward the application of artificial intelligence in clinical practice. Methods: An electronic survey on the implementation of artificial intelligence in hemodialysis clinics was distributed to 1,067 physicians. Of the 1,067 individuals invited to participate in the study, 404 (37.9%) professionals agreed to participate in the survey. Results: The survey showed that a substantial proportion of respondents believe that artificial intelligence has the potential to support physicians in reducing medical malpractice or mistakes. Conclusion: While artificial intelligence’s potential benefits are recognized in reducing medical errors and improving decision-making, concerns about treatment plan consistency, personalization, privacy, and the human aspects of patient care persist. Addressing these concerns will be crucial for successfully integrating artificial intelligence solutions in nephrology practice.

Are humanitarian supply chains sustainable? A systematic review and future research themes

PurposeTo address the challenges encountered in disaster responses, optimize resource utilization, minimize environmental and social impact, and ensure transparency and accountability, it is essential to review humanitarian supply chains and incorporate sustainability considerations. Humanitarian organizations can enhance their ability to deliver timely and effective assistance to those in need by continuously improving supply chain practices. Consequently, this work explores the convergence of two fast-growing domains: sustainability and humanitarian supply chain management (HSCM).Design/methodology/approachThe authors conducted a systematic literature review of peer-reviewed articles to identify the prominent research trends and themes from the two domains' interactions. The extant literature is represented under the theory, context, characteristics, and research method (TCCM) framework. The authors have utilized a stakeholder theory perspective to identify coordination and collaboration among the various stakeholders.FindingsThis study's review findings reveal five future research directions formulating this study's central themes: the role of environmental sustainability, coordination, and collaboration in building effective HSCs; the role of humanitarian aid for the responsive HSC; the influence of big data predictive analytics on the HSC performance; development and empirical validation of sustainable HSC performance framework; the role of HSC stakeholders in building effective and efficient HSCs.Originality/valueThere is no existing academic literature review available on sustainable HSCM. This review fills this void by fostering discussion about sustainable humanitarian supply chains where the authors notably propose the TCCM framework in the context of sustainable HSCM, followed by a stakeholder network.

Optimization of deep learning models for forecasting performance in the water industry using genetic algorithms

The objective of this study is to develop an optimization approach for performance prediction in the water industry by integrating feature selection (FS), machine learning and genetic algorithms (GAs). To select the best set of input variables, FS methods such as Pearson correlation coefficient (PCC), principal component analysis (PCA), and GAs were employed. Next, a GA method was used to optimize the hyperparameters and the architecture of a deep learning network. The methodology was tested using bi-directional long-short term memory networks for univariate prediction of effluent quality and biogas production. The results demonstrate that FS methods are not universal and depend on the desired goal. Additionally, optimized machine learning applications to wastewater process data may require a shallow hidden architecture consisting of two hidden layers with an average of 25 total neurons, resulting in models that are more computationally efficient and interpretable. This proposed methodology has a wide range of applications, including predictive big data analytics, optimal data-driven development of soft-sensors, multi-objective optimization, and model predictive controllers.

The Role of Big Data Predictive Analytics as A Mediator of The Influence of Recruitment and Selection, Remuneration and Rewards, Training, and Development on Employee Retention

The purpose of this study was to determine the effect of recruitment and selection, remuneration and rewards, as well as training and development on employee retention mediated by the big data predictive analytics variable. The method in this study uses a quantitative method which is an approach, an assessment based on numbers using statistical calculations. The samples in this study were employees of PT Addis Citra with a total of 145 respondents. The data collection technique used in this study was a questionnaire or questionnaire. While the data analysis in this study used the Structural Equation Model (SEM) with the AMOS 26 program. The research results obtained show that the variables of recruitment and selection, remuneration and rewards, and training and development have an effect on big data predictive analytics. Furthermore, big data predictive analytics variables affect employee retention, and big data predictive analytics has a mediating role in influencing recruitment and selection, remuneration and rewards as well as training and development on employee retention.

The Role of Big Data Predictive Analytics as A Mediator of The Influence of Recruitment and Selection, Remuneration and Rewards, Training, and Development on Employee Retention

The purpose of this study was to determine the effect of recruitment and selection, remuneration and rewards, as well as training and development on employee retention mediated by the big data predictive analytics variable. The method in this study uses a quantitative method which is an approach, an assessment based on numbers using statistical calculations. The samples in this study were employees of PT Addis Citra with a total of 145 respondents. The data collection technique used in this study was a questionnaire or questionnaire. While the data analysis in this study used the Structural Equation Model (SEM) with the AMOS 26 program. The research results obtained show that the variables of recruitment and selection, remuneration and rewards, and training and development have an effect on big data predictive analytics. Furthermore, big data predictive analytics variables affect employee retention, and big data predictive analytics has a mediating role in influencing recruitment and selection, remuneration and rewards as well as training and development on employee retention.

A study on high dimensional big data using predictive data analytics model

A massive bulk of data is being created due to digitalisation in various industries, including medical, manufacturing, sales, internet of things (IoT) devices, the web, and businesses. To find data patterns for data attributes machine learning (ML) algorithms are used. In this fast-growing world, we can see that data is generated in abundance by people, machines, and corporations. With the increase in computer science market, researchers are integrating heterogeneous and diverse data into accurate patterns by applying machine learning algorithms and complex strategies on data sets. The overabundance of high-dimensional big data has made it more difficult for scientists to extract important information from these data efficiently. Conventional data mining approaches are ineffective when dealing with large amounts of data. As big data increase exponentially, predictive analytics has become widely known. To evaluate a large number of data patterns, data driven technology predictive big data analytics (PBA) can be used and ML algorithms to investigate the present and future data based on the records of data patterns. In this research paper, predictive analysis on big data has been proposed using the splitting random forest (SRF) methodology with help of hyperparameter optimization and dimension reduction technique.

Integrating Predictive Big Data Analytics with Behavioral Machine Learning Models for Proactive Threat Intelligence in Industrial IoT Cybersecurity

This paper introduces a comprehensive framework for industrial Internet of Things (IoT) cybersecurity, integrating multiple algorithms to enhance threat intelligence. The proposed framework encompasses five key algorithms, each addressing specific aspects of data preprocessing, time series analysis, predictive analytics, and behavioral machine learning. The Data Preprocessing and Integration algorithm refines raw IoT data through a meticulous 20-step process, ensuring high-quality input for subsequent analyses. The Time Series Analysis algorithm delves into temporal patterns, while the Random Forest algorithm focuses on predictive analytics for proactive threat detection. The LSTM Ensemble algorithm extends the analysis into behavioral machine learning, capturing temporal dependencies and detecting anomalies. The Weighted Average Ensemble combines outputs from predictive analytics and behavioral models, leveraging their correlation for enhanced threat intelligence. An ablation study dissects the individual contributions of each algorithmic component, shedding light on their specific impacts. The results highlight the significance of each step, guiding optimizations for improved performance. The proposed framework outperforms existing methods in various performance metrics, showcasing its potential as a robust solution for proactive threat intelligence in complex industrial environments. This framework stands at the forefront of industrial IoT cybersecurity, offering a holistic and adaptive approach to address evolving threats. The ablation study enhances the transparency and understanding of the framework, contributing to its continuous refinement and effectiveness in safeguarding critical industrial systems.

THE FUTURE OF BIG DATA ANALYTICS AND ITS PROGRESS

Big data streams are now widespread because real-time applications that generate enormous amounts of data quickly have proliferated during the past 10 years. Big data is a treasure trove of vast quantities of data. Big data is commonly used in conjunction with other current buzzwords like data science, artificial intelligence, deep learning, and machine learning. Big data has a very promising future because of the demand for data across many technical industries. A massive amount of data has been produced as a result of the COVID-19 global epidemic. Big data analytics is helpful for evaluating, maintaining, and archiving information about COVID-19 patients for later use during the pandemic phase. Big Data Analytics (BDA), a growingly important technology, can significantly enhance sustainable tourism in terms of destination selection, traveller satisfaction, and the quality of travel experiences. Streaming Big Data is the term used to describe data that needs to be saved, processed, and analysed and that is continuously generated from various data sources, including Internet of Things (IoT) devices. The article provides information on - Big data analytics techniques in social media, COVID-19 Pandemic in the New Era of Big Data Analytics, Big data analytics and sustainable tourism, Predictive big data analytics for supply chain demand forecasting, Streaming Big Data Analytics, Big data stream analysis and Big Data Analytics in Crypto Currency. Keywords: Big Data, Streaming Big Data, Predictive Big Data, Sustainable Tourism

Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)

Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented in this paper, 681 non-duplicate publications are retrieved from databases of Web of Science Core Collection (WoSCC) and Scopus from 2000 to 2021. The countries, institutions, cited authors, cited journals, and cited references with the most academic contributions were identified. Social networks and collaborations between countries, institutions, and scholars are explored. The cross degree of disciplinaries is measured. The hotspot distribution and burst keyword historic trend are explored, where research methods, BI-based applications, and challenges are separately discussed. Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. Findings show that big data and AI-based methods for BI are one of the most popular research topics in the next few years, especially when it applies to topics of COVID-19, healthcare, hospitality, and 5G. Thus, this study contributes reference value for future research, especially for direct selection and method application.

Retraction Note to: Optimizing appointment scheduling for out patients and income analysis for hospitals using big data predictive analytics

Retraction Note to: Optimizing appointment scheduling for out patients and income analysis for hospitals using big data predictive analytics

Faster as Well as Early Measurements from Big Data Predictive Analytics Model

Data processing in large scale system and analyses is the key problems of today's distributed systems in real-time and close to real time. These systems should be able, while meeting these constraints, to process large-scale data inputs, to expand to terabytes or larger. Volume of processing the data over the systems of large scale becomes too large. These systems use parallelization of the data and similar techniques to increase performance. The standards are increasing, though: data analyses are expected almost in real time. Among all the data treatment models Map Reduce is mostly adopted. Due to the processing of data design in this method, once work is finished, the results are returned. The growth of data is not always a preferred batch operating environment: large numbers of applications can benefit from early results. The online aggregation method used in connection databases was first discussed. In this work analysis we discuss techniques that can allow early results to be estimated. We are proposing some changes to the online system for Map Reduce. We show that the properties required for accurate evaluation of the effects of our proposed device design changes. The reduction of data bias algorithm and sampling at the block levels are provided. Consequently, with a series of particular applications and sets of data, the implementation of the proposed design of the system is defined and evaluated.

Predictive Big Data Analytics for Service Requests: A Framework

Nowadays, valuable big data are generated and collected rapidly from numerous rich data sources. Following the initiatives of open data, many organizations including municipal governments are willing to share their data such as open big data regarding non-emergency city service requests from their residents. Big data analytics on these open big data can be for social good. Hence, in this article, we present a framework for predictive big data analytics for service requests. The framework mines historical open big data to discover patterns about service requests, and predicts the demands for these services in the future. The discovered knowledge and predictions may provide policy makers deeper understanding of their data and requested services so that appropriate actions could take place. Evaluation on open big data from the City of Winnipeg demonstrates the usefulness of our framework for conducting predictive big data analytics for service requests.

Internal control as a tool for anti-crisis stability of metallurgical companies

The subject of the study is the tools of internal control in the context of digitalization to ensure the anti-crisis stability of Russian ferrous metallurgy companies. The aim of the work is to study the conditions for effective digitalization of the internal control system based on innovations and electronic document management. The novelty of the conducted research consists in the presentation of practice-oriented recommendations on the stages of decision-making on the use of digital technologies in the internal control system, taking into account the peculiarities of the metallurgical industry. The relevance of the research presented in this article is due to the fact that the transformation of the internal control system of a metallurgical company provides a mechanism for eliminating emerging risks, as well as economic security in conditions of uncertainty. The conducted theoretical and applied analyses cover the study of factors that can ensure the effectiveness of the internal control system based on digital solutions for big data analysis and predictive analytics. The practical significance of the study is determined by the possibility of using the conclusions and proposals contained in it, which will allow the management of the metallurgical company to quickly identify financial risks, plan the means of their elimination and carry out appropriate follow-up control. To achieve this goal, the approach of using remote methods in a remote format based on digital technologies was justified. This will allow business owners to determine the current state of affairs and electronic document management in real time. It is concluded that it is digitalization that makes internal control an effective tool for ensuring the anti-crisis stability of metallurgical companies.

The impact of Big Data Predictive Analytics on the performance of Asian firms: Does the supply chain effectiveness matter?

The purpose of this research is to evaluate the influence that big data predictive analytics have on the performance of Asian companies and to determine whether or not the efficiency of the supply chain has any bearing on this relationship. The use of a quantitative research design allowed for the collection of data from a representative cross-section of Asian businesses. The findings suggest that big data predictive analytics have a beneficial impact on company performance, and the findings also suggest that the relationship between these two factors is increased by the efficiency of the supply chain. According to the findings, businesses in this region may be able to improve their performance by utilizing big data predictive analytics, particularly when combined with efficient supply chain management. In addition to this, the study sheds light on the significance of effective supply chain management in maximizing the beneficial effects of big data analytics on business performance. This study makes a contribution to our understanding of how the effectiveness of supply chain management and big data predictive analytics play a role in boosting the performance of Asian companies. The findings have repercussions for managers and policymakers in the region, and they underline the necessity to prioritize investments in big data predictive analytics and supply chain management in order to improve business performance.
 

Exploring a nexus among the big data Predictive analytics, Supply Chain Performance, and Organizational Performance: A case of an Asian country

The goal of this study is to look into the relationship between big data predictive analytics, supply chain performance, and organizational performance in an Asian country. The study employs a variety of approaches, including quantitative data analysis and qualitative interviews with experts in the subject. According to the findings, applying predictive analytics to big data has a positive impact on the performance of supply chains, which in turn has a positive impact on the performance of companies. According to the poll, the application of big data analytics in supply chain management is not yet widespread in the region. Instead, many organizations continue to rely on more traditional techniques of managing their supply chains. According to the findings, businesses in the region may benefit from applying big data predictive analytics to improve the performance of their supply chains and organizations. In overall, the findings of this study contribute to a better understanding of the interaction between big data, supply chain performance, and organizational performance in the context of an Asian nation.

Responsible governance mechanisms and the role of suppliers’ ambidexterity and big data predictive analytics capabilities in circular economy practices improvements

Recent years have seen a growing interest in the separate areas of responsible governance mechanisms and circular economy practices. However, the literature lacks evidence on supplier capabilities as a responsible governance mechanism pathway for buyer firms to improve their circular economy practices. Drawing on the natural resource-based view, and the dynamic capabilities perspective, this study examines suppliers’ supply chain ambidexterity and big data predictive analytics capabilities as enabling factors in support of buyer firms’ responsible governance mechanism for circular economy practices improvements. Using data from surveys conducted among 404 firms (202 focal firm-supplier dyads), the results suggest that the focal firm’s supplier-selection mechanism significantly and positively influences its circular economy practices while the supplier-development mechanism has no such effect. We find further that the governance mechanisms vary in their effects on suppliers’ supply chain ambidexterity and subsequent big data analytics capabilities and circular economy practices improvements outcomes. In sum, our findings provide unique insights into the role of suppliers’ capabilities for responsible governance mechanisms to circular economy practices improvements from the perspective of the dyad.

A digitally enabled circular economy for mitigating food waste: Understanding innovative marketing strategies in the context of an emerging economy

Within the context of the circular economy, this article analyses how supermarkets can reduce food waste by implementing appropriate marketing strategies underpinned by digital technologies, such as Big Data predictive analytics. Little is currently known of the potential role of the emerging digital technologies in furthering the sustainability of supermarket chains in emerging economies. Thus, an original multiple case study has been conducted, involving six supermarket chains operating in Brazil and six farming distributors that produce fruit and vegetables (F&V). The findings show that actions towards food waste reduction based on the principles of the circular economy begin with an understanding of F&V stages of deterioration. Through such an understanding, a number of actions can be undertaken to reduce the negative effects of this deterioration. These include (among others) the management of prices, sales, operations, and purchases, all of which can be underpinned by such technologies as sensors and augmented reality in order to manage dynamic pricing, storage, and item display. The findings contribute to demonstrating how digital technologies can help supermarkets’ marketing departments in driving corporate sustainability while also benefiting both consumers and societal well-being.