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Companies are investing in big data analytics capabilities as they look for ways to understand and innovate their business models by leveraging digital transformation. We explore this phenomenon from the perspective of retail grocery business where evolving consumer attitudes and behaviors, rapid technological advances, new competitive pressures, laser thin margins, and the COVID-19 pandemic have accelerated the pace of digital transformation. We specifically analyze the role of big data analytics capabilities of the top five grocery companies in the United States in light of their digital transformation initiatives. We find that retailers are making major investments in big data analytics capabilities to power all aspects of their digital ecosystem-the online shopping experience for the digital consumer, digital store operations, pickup and delivery mechanisms-to enhance shopping experience, customer loyalty, revenue, and ultimately profit. © 2022 IEEE Computer Society. All rights reserved.
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Consider the most important lessons learned from the global achievements and disappointments of the previous year. It was a year filled with pandemics that exacerbated massive geopolitical, social, and economic shocks on a worldwide scale, bringing out the worst and best in people. However, the past two years have demonstrated the fragility of global institutions in numerous industries, including medicine, hospitality, travel, and commerce. It also reflects the resilience of the international system with the introduction of various vaccinations and concentrated worldwide efforts against pandemic threats. Conventional and cutting-edge technology approaches are needed to attack COVID-19 and put the situation under control. This paper's primary purpose is to systematically study trends in technology solutions for smart healthcare systems - for example, artificial intelligence (AI) and big data (BD) analytics, which will help save the world. These AI solutions facilitate innovative administrations, adaptability, productivity, and efficiency by developing related frameworks. Specifically, this study identifies AI and Big Data contributions that should be incorporated into smart healthcare systems. It also studies the application of big data analytics and AI to offer users insights and help them to plan and presents models for intelligent healthcare systems based on AI and big data analytics. © 2023 IEEE.
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BACKGROUND: Traditional clinical trials require tests and procedures that are administered in centralized clinical research sites, which are beyond the standard of care that patients receive for their rare and chronic diseases. The limited number of rare disease patients scattered around the world makes it particularly challenging to recruit participants and conduct these traditional clinical trials. MAIN BODY: Participating in clinical research can be burdensome, especially for children, the elderly, physically and cognitively impaired individuals who require transportation and caregiver assistance, or patients who live in remote locations or cannot afford transportation. In recent years, there is an increasing need to consider Decentralized Clinical Trials (DCT) as a participant-centric approach that uses new technologies and innovative procedures for interaction with participants in the comfort of their home. CONCLUSION: This paper discusses the planning and conduct of DCTs, which can increase the quality of trials with a specific focus on rare diseases.
Subject(s)
Caregivers , Rare Diseases , Aged , Child , Humans , Clinical Trials as TopicABSTRACT
In recent times, the amount of data sent and received through wireless networks has grown quickly. Smartphones and the growth of Internet access around the world are two big reasons for this volume. Due to the current state of global health, which is mostly caused by Covid-19, telecommunications companies have a great chance to find new ways to make money by using Big Data Analytics (BDA) solutions. This is because data traffic has gone up. After all, more customers are using telecommunications services. As most of the world's data is now made by smartphones and sent through the telecom network, telecom operators are facing an information explosion that makes it harder to make decisions based on the data they need to predict how people will act. This problem was solved by making a system that sorts through information and makes suggestions based on how people have behaved in the past. Content-based filtering, collaborative filtering, and a hybrid approach are the three main ways that recommender systems filter data to solve the problem of too much data and give users relevant recommendations based on their interests and the data that is being created in real-time. Distance algorithms like Cosine, Euclidean, Manhattan, and Minkowski are at the heart of the suggested recommender system, which aims to research and design an effective recommendation strategy. The suggested model suggests different telecom packages to meet the needs of users to increase revenue per subscriber and get consumers, telecom providers, and corporations to sign long-term contracts. © 2022 IEEE.
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Business performance has increased dramatically owing to the increase use of multichannel services in global markets posed by COVID-19 pandemic new normal. The viability of commercial airports depends on strong business models that integrates multichannel services such as digital services, cloud services and big data analytics services to its building blocks. This paper examines how multichannel services and big data analytics services can be used to optimize and enhance airport business building blocks to its business models to increase airports' revenue and business value. A case study was conducted to analyze PNG's airport authority's (National Airports Corporation (NAC) existing business models in comparison to the Osterwalder's business model building blocks. A sustainable business model was proposed that integrates digital and web-based technologies to boost the model's commercial and operational viability. © 2022 ACM.
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This paper focuses the attention on a real-life case study represented by the design, the development and the practice of OLAP tools over big COVID-19 data in Canada. The OLAP tools developed in this context are further enriched by machine learning procedures that magnify the mining effect. The contribution presented in this paper also embeds an implicit methodology for OLAP over big COVID-19 data. Experimental analysis on the target case study is also provided. © 2022 IEEE.
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Over the last couple of years, Covid-19 has caused an uproar and chaos throughout the world. The occurrence of Covid-19 is an unprecedented event which has led towards a substantial number of lost humans' lives and mayhem in the economic, social, and most importantly, healthcare systems across the world. In order to gain control of the pandemic, it is extremely pertinent to truly grasp the characteristics and behavior of the coronavirus which can be done by gathering and evaluating related big data. Furthermore, big data analytics tools are known to play an important role in building knowledge which are vital for decision making and precautionary measures. Across the world, it is evident that both government and non-governmental organizations have been working hand-in-hand to deploy big data technology. There is a plethora of data analytics methods available thus, the intention of this work is to assemble available methods which can be applied in the current pandemic. © 2022 ACM.
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In the current era of big data, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the willingness of many government, researchers, and organizations are led by the initiates of open data to share their data and make them publicly accessible. Healthcare, disease, and epidemiological data, such as privacy-preserving statistics on patients who suffered from epidemic diseases such as Coronavirus disease 2019 (COVID-19), are examples of open big data. Analyzing these open big data can be for social good. For instance, people get a better understanding of the disease by analyzing and mining the disease statistics, which may inspire them to take part in preventing, detecting, controlling and combating the disease. Having a pictorial representation further enhances the understanding of the data and corresponding results for analysis and mining because a picture is worth a thousand words. Hence, in this paper, we present a visual data science solution for the visualization and visual analytics of big sequential data. The visualization and visual analytics of sequences of real-life COVID-19 epidemiological data illustrate the ideas. Through our solution, we enable users to visualize the COVID-19 epidemiological data over time. It also allows people to visually analyze the data and discover relationships among popular features associated with the COVID-19 cases. The effectiveness of our visual data science solution in enhancing user experience in the visualization and visual analytics of big sequential data are demonstrated by evaluation of these real-life sequential COVID-19 epidemiological data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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In the age of the digital society formation in countries of the post-industrial development stage, digital transformations take place in all spheres of public life. Such social institution as education witnesses especially drastic changes. New learning formats are emerging, including Online learning, E-learning, Smart-learning, Smart Education and other distance learning forms. However, during the COVID-19 pandemic, the scientific and educational community updated a number of issues regarding the identification of works, assessing the level of comprehensibility of the discipline and the independence of completing individual tasks of distance learning students. The present research is intended to complement the range of relevant studies in this area as it is devoted to the methodology of Big Data Analytics and e-proctoring at higher education institutions. The results of this study will help to improve the understanding of the algorithm for the final certification of students. In addition, the subjects of the educational process will be able to improve their skills in solving the problems of conducting final and intermediate certification in the format of distance learning and optimize this process in educational institutions of various levels. © 2022 IEEE.
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The pandemic COVID-19 is an infectious disease discovered first in China on December 19 and spread very fast over countries in the world where millions of people are infected. This is a deadly virus, which affects each facet of everyday lives. The novel leading Big Data applications have provoked in several areas are utilized in outbreak prediction, tracking of virus spread and prevent by the diagnosis of COVID-19. In addition, the techniques of Machine Learning (ML) have been employed commonly for various domains, which are already a huge market to ML-aided diagnostic systems in COVID-19 monitoring, predicting of virus spread and diagnosis or treatment of COVID-19 to determine the potential cure. Hence, this research focused on maintaining its significance in leading to the outbreak of COVID-19 and in mitigating the serious possessions of COVID-19. Initially, this paper has presented an outline of COVID-19 followed by the application of big data and ML towards fighting against COVID-19. Subsequently, it highlights the problems and challenges related to advance d solutions which help in finding out the advantage and disadvantages of recent techniques for controlling an efficient contract tracking and generating an outbreak of the COVID-19 situation. In this paper correlation matrix tool is proposed to identify the disease with minimal features. Only the test is being used to evaluate the condition because symptoms are many and inaccurate. The detection of disease is much enhanced by combining a machine learning predictive model with a correlation matrix tool. A correlation matrix is a technique that is used in the analysation of certain attributes. The correlation value between the feature values is determined, which improves the accuracy of the output. © 2022 IEEE.
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BACKGROUND: As investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote quality assurance (QA) activities for clinical trials, we developed new quality indicators, building on a previously published statistical modeling methodology. METHODS: We modeled the risk of having an audit or inspection finding using historical audits and inspections data from 2011 to 2019. We used logistic regression to model finding risk for 4 clinical impact factor (CIF) categories: Safety Reporting, Data Integrity, Consent and Protecting Endpoints. RESULTS: We could identify 15 interpretable factors influencing audit finding risk of 4 out of 5 CIF categories. They can be used to realistically predict differences in risk between 25 and 43% for different sites which suffice to rank sites by audit and inspection finding risk. CONCLUSION: Continuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies for clinical trials and help to manage audit targets and audit focus also in post-pandemic times.
Subject(s)
COVID-19 , Pandemics , Clinical Trials as Topic , Follow-Up Studies , Humans , Models, Statistical , Risk AssessmentABSTRACT
The COVID-19 pandemic severely impacted residential care delivery all around the world. This study investigates the current scheduling methods in residential care facilities in order to enhance them for pandemic conditions. We first define the basic problem that addresses decisions associated with the assignment and scheduling of staff members, who perform a set of tasks required by residents during a planning horizon. This problem includes the minimization of costs associated with the salary of part-time staff members, total overtime, and violations of service time windows. Subsequently, we adapt the basic problem to pandemic conditions by considering the impacts of communal spaces (e.g., shared rooms) and a cohorting policy (classification of residents based on their risk of infection) on the spread of infectious diseases. We introduce a new objective function that minimizes the number of distinct staff members serving each room of residents. Likewise, we propose a new objective function for the cohorting policy that aims to minimize the number of distinct cohorts served by each staff member. A new constraint is incorporated that forces staff members to serve only one cohort within a shift. We present a population-based heuristic algorithm to solve this problem. Through a comparison with two benchmark solution approaches (a mathematical programme and a non-dominated archiving ant colony optimization algorithm), the superiority of the heuristic algorithm is shown regarding solution quality and CPU time. Finally, we conduct numerical analyses to present managerial implications.
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The COVID-19 impacts go beyond healthcare systems as they also challenge global markets and society. A comprehensive knowledge involving the elements to contain the virus is fundamental for properly planning and implementing a quick response to the problems faced worldwide. Learning to coexist with the COVID-19 pandemic has become part of our daily life. Hence, the scientific community’s capabilities to continuously provide solutions for pandemics are crucial to mitigate the spread of the pandemic. The main contribution of this work is to propose applications of advanced analytics (AA) in healthcare treatment networks that predict epidemiology curves and the distribution of patients’ severity towards. These tools assist the optimization of such networks with innovative solutions aiming to increase the capacity, responsiveness, and preparedness of the infrastructure and management in healthcare systems. Such a decision-making environment can forecast the spread of the disease by utilizing given inputs such as social distance, out-of-stock of personal protective equipment (PPE) items, lockdown policies, environmental factors, etc. These forecasts are especially important to allow a) medical corporations to design and operate healthcare treatment systems and b) governments to develop policies aiming to maintain the balance between social progress and a sustainable economy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Technological advancements have made it easy and quick to generate and collect huge volumes of varieties of data from wide ranges of rich data sources. These big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by big data science and analysis for social good. In this paper, we propose a solution to analyze coronavirus disease 2019 (COVID-19) epidemiological data. In particular, the solution focuses on analyzing valuable information and useful knowledge (e.g., distribution, frequency, patterns) of health-related states and characteristics in populations. Discovered information and knowledge helps users (e.g., researcher, civilian) to understand the disease better, and thus take an active role in fighting, controlling, and/or combating the disease. Evaluation of our solution on real-life data demonstrates its practicality in analyzing COVID-19 epidemiological data and revealing demographic relationships among COVID-19 cases. © 2021 IEEE.
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The world is headed towards a new normal, while pandemic is continuing with new waves hitting nations. However, this storm will pass, but the choices we make now could change our lives for years to come. It is crucial to use new research perspectives bringing scientists and practitioners together to support decisions that would shape our future. This paper gives an example of consumer neuroscience and predictive analytics helping to go beyond rational verbatims to investigate real deep nonconscious convictions that people may not even be fully aware of and which cannot be covered by traditional opinion surveys. For example, public confidence in the healthcare system is related to pro-social behaviour and compliance with non-pharmaceutical interventions during a crisis. The success in controlling pandemics depends on behaviour, and health officials need to persuade the population to make behaviour changes to ensure success. By providing comparative results from Portugal, this study highlights what changed in one year and what the population is not willing to admit by using the “COVID-19 Fever” project data collected and analysed with the iCode Smart Test in 2020 and 2021 and offers valuable data to support an effective communication strategy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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This study is designed to discover current energy-related research trends as evidence inherent in big data and obtain future agendas and new insights to reshape global energy strategy considering environmental change globally in the era of COVID-19. To this end, vast amounts of unstructured text data on energy demand from 4 top journals in the energy field over the past year (2020–2021) were collected. This study used a semantic network analysis in big data analytics. As a result, this study obviously shows that research evidence on traditional energy sources such as fossil fuels and gas is still essential in the current situation where climate change and global warming are intensifying worldwide. Simultaneously, research on renewable energy is positioned as a critical agenda by providing sufficient evidence to draw practical implications concerning overall global energy companies' strategy and each government's new energy policy in line with the rapid changes in the global environment. Consequently, this study proposes that a decision-maker or leader can exert the remarkable power of reshaping global energy strategy for future sustainability, taking into account the recognizable trends inherent in big data. © 2022 The Author
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Technology has changed the face of almost every sphere of life including the healthcare system which was erstwhile considered a subject of pure clinical evaluation. The onset of the Covid 19 pandemic underscored adaptation to technology as the singular solution to address the banes of the huge number of people afflicted with it globally with limited healthcare workers and resources. The opening of a small healthcare centre in the Al Ras region marked the beginning of healthcare in Dubai. The healthcare system of the UAE has grown exponentially over the years, and it now offers a variety of specialized services that sets it apart from the others. The UAE healthcare industry is increasingly improving to meet the changing needs of its people as well as the country's goal to become a regional medical tourism hub. The government is implementing several long-term projects to achieve balanced growth and to incorporate sustainable growth in the industry while meeting the immediate needs of the population. Comprehensive health-care reforms have been enacted over the last ten years. This paper examines the development and results of Health-Care reforms in the UAE using primary data collected by conducting a survey in a prominent health organization in the UAE. The main aim of the survey was to study the impact of digitalization on the healthcare sector in the UAE. The case in point was the influence of digitalized measures including Big Data Analytics and Artificial Intelligence (AI) in handling the Covid-19 situation from the point of view of the healthcare sector employees. © 2021 IEEE.
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Currently, we are in the COVID-19 pandemic situation, where the healthcare sector plays a major role to prevent the loss of life across the globe. During this COVID-19 period, the remote healthcare assistance and monitor systems show their impotence and existence. Science and technology are always associated with the healthcare sector, and it helps to diagnose the cause of bad health and helps to improve by proper treatment. The analysis of the causes plays a major role in treatment. With the advance of science and technology for the complex clinical solution in healthcare, IoHT (internet of health care things) and intelligent analytics in big data can play a bigger role in cancer detection, brain tumor, etc. The main focus of our work is to discuss a framework of intelligent analytics for big data utility to such a complex clinical problem on the IoHT platform. The digital imaging of medical imaging is playing a critical role in the clinical operation of a human in healthcare. The treatment of cancer or the brain is a very complex multistage treatment process;here, use of intelligent big data analytics and IoHT can help the doctors to provide better decision making which helps in treatment and recovery stages. So the remote healthcare sector, which is showing its utility during the COVID-19 situation, where intelligent data analytics play a major role to provide better healthcare solutions and monitoring to the human society. The intelligent medical systems for healthcare need proper data acquisition health condition, which need to analyze, store and process for each individual for clinical treatment and future reference. The past information periodically and present health condition can store and analyzed for better healthcare, this leads to an intelligent database system, where Intelligent data analytics play a big role, with the utility of the advanced platform it becomes robust and can be the backbone of the healthcare sector. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The coronavirus disease 2019 (COVID-19) is an infectious disease with high transmissibility and acquired through the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Scientists, physicians, and health officials are seeking innovative approaches to understand the complex COVID-19 pandemic pathway and decrease its morbidity and mortality. Incorporating artificial intelligence and data science techniques across the health science domain could improve disease surveillance, intervention planning, and policymaking. In this paper, we report our effort on the deployment of multimodal big data analytics to improve pandemic surveillance and preparedness. A common challenge for conducting multimodal big data analytics in clinical and public health settings is the issue of the integration of multidimensional heterogeneous data sources. Additional challenges for developers are explaining decisions and actions made by intelligent systems to human users, maintaining interpretability between different data sources, and privacy of health information. We present Urban Population Health Observatory (UPHO), an explainable knowledge-based multimodal data analytics platform to facilitate CoVID-19 surveillance by integrating a large volume of multimodal multidimensional, heterogenous data including social determinants of health indicators, clinical and population health data. © 2021 IEEE.
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The global cloud market has recently increased dramatically due to the Covid-19 pandemic. In such situation, people are looking for flexibility to work from home without going to the office physically and hence many organizations have started to look for a cloud-based solution. Clearly, Cloud not only offers flexibility, but also provides scalability and availability to an organization especially for startups and SMEs which have not established a firm and stable architecture yet. Hence, this paper aims to provide a better understanding on the model, features, and services to select the most suitable cloud platform based on the objectives or requirements of an organization. Finally, potential challenges of cloud development are also described to assist organizations in deploying cloud as a service in order to make a wise decision beforehand as it would be a long-run processes. © 2021 IEEE.