Big Data Analytics Tools Research Articles

Implementing Big Data Analytics in E-Commerce: Vendor and Customer View

Due to the information revolution, one of the biggest challenges of e-commerce is the huge data that needs to be processed and analyzed to gain benefits from it. Big Data Analytics (BDA) aims to improve the decision-making process by analyzing and understanding big data, e.g., messages, social media posts, etc. Furthermore, BDA capabilities are used in e-commerce activities as a key growth direction to increase vendors' revenues and attract customers. Based on the importance of analyzing big data and its advantages to e-commerce operations, this paper aims to study the values of implementing BDA in e-commerce to both vendors and consumers. Fifteen papers are selected to analyze the impacts of analyzing big data in e-commerce. Electronic vendors (E-vendors) use BDA to gain the competitive advantages they need to understand consumer behaviour and increase their income by improving customer loyalty. Besides, recommendation systems derived from BDA personalize the searching and shopping experience of the customers. However, there are some negative effects derived from applying BDA in e-commerce, such as shopping addiction. In addition, e-vendors have to deal with expensive BDA tools and professionals. In conclusion, even though BDA enhances consumers' and vendors' electronic shopping experience, the rapid growth of data is still challenging.

Induced bioresistance via BNP detection for machine learning-based risk assessment

Machine Learning (ML) is a powerful tool for big data analysis that shows substantial potential in the field of healthcare. Individual patient data can be inundative, but its value can be extracted by ML's predictive power and ability to find trends. A great area of interest is early diagnosis and disease management strategies for cardiovascular disease (CVD), the leading cause of death in the world. Treatment is often inhibited by analysis delays, but rapid testing and determination can help improve frequency for real time monitoring. In this research, an ML algorithm was developed in conjunction with a flexible BNP sensor to create a quick diagnostic tool. The sensor was fabricated as an ion-selective field effect transistor (ISFET) in order to be able to quickly gather large amounts of electrical data from a sample. Artifical samples were tested to characterize the sensors using linear sweep voltammetry, and the resulting data was utilized as the initial training set for the ML algorithm, an implementation of quadratic discriminant analysis (QDA) written in MATLAB. Human blood serum samples from 30 University of Pittsburgh Medical Center (UPMC) patients were tested to evaluate the effective sorting power of the algorithm, yielding 95% power in addition to ultra fast data collection and determination.

Big Data-Enabled Analysis of DRGs-Based Payment on Stroke Patients in Jiaozuo, China.

Stroke is the first leading cause of mortality in China with annual 2 million deaths. According to the National Health Commission of the People's Republic of China, the annual in-hospital costs for the stroke patients in China reach ¥20.71 billion. Moreover, multivariate stepwise linear regression is a prevalent big data analysis tool employing the statistical significance to determine the explanatory variables. In light of this fact, this paper aims to analyze the pertinent influence factors of diagnosis related groups- (DRGs-) based stroke patients on the in-hospital costs in Jiaozuo city of Henan province, China, to provide the theoretical guidance for medical payment and medical resource allocation in Jiaozuo city of Henan province, China. All medical data records of 3,590 stroke patients were from the First Affiliated Hospital of Henan Polytechnic University between 1 January 2019 and 31 December 2019, which is a Class A tertiary comprehensive hospital in Jiaozuo city. By using the classical statistical and multivariate linear regression analysis of big data related algorithms, this study is conducted to investigate the influence factors of the stroke patients on in-hospital costs, such as age, gender, length of stay (LoS), and outcomes. The essential findings of this paper are shown as follows: (1) age, LoS, and outcomes have significant effects on the in-hospital costs of stroke patients; (2) gender is not a statistically significant influence factor on the in-hospital costs of the stroke patients; (3) DRGs classification of the stroke patients manifests not only a reduced mean LoS but also a peculiar shape of the distribution of LoS.

Supervised clustering of high-dimensional data using regularized mixture modeling.

Identifying relationships between genetic variations and their clinical presentations has been challenged by the heterogeneous causes of a disease. It is imperative to unveil the relationship between the high-dimensional genetic manifestations and the clinical presentations, while taking into account the possible heterogeneity of the study subjects.We proposed a novel supervised clustering algorithm using penalized mixture regression model, called component-wise sparse mixture regression (CSMR), to deal with the challenges in studying the heterogeneous relationships between high-dimensional genetic features and a phenotype. The algorithm was adapted from the classification expectation maximization algorithm, which offers a novel supervised solution to the clustering problem, with substantial improvement on both the computational efficiency and biological interpretability. Experimental evaluation on simulated benchmark datasets demonstrated that the CSMR can accurately identify the subspaces on which subset of features are explanatory to the response variables, and it outperformed the baseline methods. Application of CSMR on a drug sensitivity dataset again demonstrated the superior performance of CSMR over the others, where CSMR is powerful in recapitulating the distinct subgroups hidden in the pool of cell lines with regards to their coping mechanisms to different drugs. CSMR represents a big data analysis tool with the potential to resolve the complexity of translating the clinical representations of the disease to the real causes underpinning it. We believe that it will bring new understanding to the molecular basis of a disease and could be of special relevance in the growing field of personalized medicine.

Preface

The fields of magnetohydrodynamics (MHD) and plasma physics might appear esoteric at first glance, but dig a little deeper, and one realizes that many of the most fundamental challenges facing mankind are underpinned by these processes. Examples range from the design of controlled thermonuclear fusion reactors as an ecologically-friendly approach to renewable energy, to tornado and vortex generation from a climate science perspective, and all the way to understanding the origin of the chemical elements within the hearts of dying stars. The breadth of these applications, the algorithm development required to model such processes, and the associated Big Data analysis tools, are inexorably linked and contribute to the philosophy of this year’s MHD and Plasma Physics Workshop. Our goal in organizing the 3rd in this series of Workshops has been to provide opportunities for these apparently disparate communities to both highlight their recent developments and discuss the future of algorithm co-design optimized for accuracy, scalability, and efficiency. This year, we have all worked and lived with the daily threat of Covid19; the need for social distancing and relative isolation has impacted upon the familiar and fundamental nature of conferences. Regardless, such interactions are the lifeblood of our respective communities, and so we made the decision to proceed with the Workshop, bring participants together from around the world through a digital platform.

Big data augments emotional labour and organisational outcomes

Marketing managers and practitioners believe that effective emotional labour strategy and big data techniques are essential elements for firms to optimise customer service encounters. However, no common understanding of emotional labour in a big data context exists, and knowledge about how big data techniques affect emotional labour's service delivery is limited. In this study, I use emotional regulation and role/script theories to propose ways in which surface acting decreases and deep acting increases customers' perceived customer orientation (PCO), resulting in enhanced word of mouth (WOM). Because big data analytics tools provide information to shield employees from emotional dissonance and depletion, this study further proposes that big data analytics will augment the aforementioned benefits. However, the influence of big data analytics may depend on a firm's data verification ability. Lastly this study suggests that firms strengthen their data verification capability to achieve the big data bonus.

Application of Big Data Technology in Product Selection on Cross-border E-commerce Platforms

With Amazon as the study case, the application of big data in product selection on this e-commerce platform is studied. Two big data analysis tools commonly used in commerce, the MPP distributed database and the Hadoop distributed database, were analyzed. Based on big data technology, the search function of the platform, the analytical tools, and third-party data analytical tools, this study compared different levels of comments of customers for the same type of products and analyzed the product selection mechanism.

Research on Cellular Wireless Location Algorithm Considering TDOA Location Technology under the Background of Network Big Data Fusion

The background and foundation of applying big data analysis to mobile communication network planning is precisely the current rapid growth of network data and the update and development of big data technology. With the help of big data analysis tools, based on the current communication network, planning goals, it can be more comprehensive Analyze and explore the value perception and behavior trajectory of network users, etc., so as to determine the market demand trend, formulate a more scientific and reasonable planning program. In short, the purpose of the application of big data in communication network, planning is to promote the healthy development of the network. The cellular wireless positioning algorithm that realizes TDOA positioning technology by using big data is a relatively important technology, which is studied in this paper.

Exploring Ethical Dilemma in Big Data Analytics: A Literature Review

Big data analytics has remarkably transformed every sector of the world, including the Emergency Service sector, health sector, science, and space, as well as the business sector, in the way they manage and run their activities every day. Big data analytics has made it possible and easier for all of these sectors to predict future outcomes, make more adequate critical decisions, and take more effective actions respectively. It now gives businesses the power to obtain, access, and leverage customers' data, and even control their behaviors. Many sectors are leveraging big data in that respect, especially businesses. They are incorporating big data analytical tools to obtain their users' data and access their personal information, such as their interests, behavioral pattern, ideas, security details, personal interactions, etc, which they store and leverage for their benefits. This information is obtained and accessed usually without the conscious knowledge and approval of their consumers. This is the major factor that has raised ethical concerns among several leaders and experts about the extent of the personal information that can be accessed, as well as the safety and wellbeing of the common people who are unaware that their personal information is being obtained, accessed, and used. Another concern is about the freedom of individuals. With the innovation and implementation of big data analytics, many of the businesses can technically control their users; what they want, need, and do, and thus, depriving them of the freedom to make decisions on their own. There is also another concern concerning the safety of the intellectual property of individuals and corporations. Big data analytics can lead to intellectual property infringement and theft, thereby denying people and bodies the right to their ideas and innovations. While on one hand big data analytics yield great promises, on the other hand, it raises critical ethical (security and privacy) issues, which if left unaddressed may become significant barriers to the fulfillment of expected opportunities and long-term success of big data. In this paper, we discuss the ethical dilemmas in big data analytics.

On the innate immune response to intracellular bacterial infections

Various bacteria are able to infect human organisms to induce less or more dangerous diseases. In response to them, the human immune system is usually activated. In addition, medical therapies to the patients can be applied. One of the most popular approaches is the use of various antibiotics. However, some bacteria are able to develop resistance against the applied antibiotics. Various approaches to study these phenomena are proposed in the literature, among them tools for big data analysis, machine learning, mathematical modeling, etc. In our paper, we consider a mathematical model describing the interactions between susceptible uninfected cells, infected cells, immune cells and bacteria. We study the properties of the model. We propose numerical scheme for obtaining the approximate solutions to the model system for various values of parameters and initial conditions.

Teeth infection and fatigue prediction using optimized neural networks and big data analytic tool

Despite the rapid improvement in dental health over the last few decades, a significant portion of our population continue seek dental care every year. Estimates show that 13% of adults seek dental care for dental infection or fatigue within four years. The Social and individual burden of this disease can be reduced by its early detection. However, the symptoms of teeth infection in the early stages are not clear, hence, it would be relatively difficult to predict teeth infections based solely on human skills and experience. Big Data (BD) technologies have a great potential in transforming dental care, as they have revolutionized other industries. In addition to reducing cost, they could save millions of lives and improve patient outcomes. This paper proposes a novel integrated prediction model that extracts hidden knowledge from radiographic datasets containing a large volume of dental X-ray images and utilizes this knowledge to predict dental infections. Initially, preprocessing techniques using morphological skeleton and mean approach is applied to eliminate noise and enhance the images. Next, Multi Scale Segmented Region (MSR) approach, Watershed Approach (WA), Sobel edge Detection (SD), Histogram based Segmentation (HS), Trainable Segmentation (TS), Dual Clustering (DC), and Fuzzy C-Means clustering (FCM) are examined for image segmentation and feature extraction. Among these methods, MSR was selected for feature extraction since it outperformed other methods in terms of accuracy, specificity, precision, recall and F1-score. Then, a set of neural network classifiers are trained to identify patterns in the extracted optimized features and predict dental infections. For this purpose, we have examined Bacterial Optimized Recurrent Neural Networks (BORNN), Deep Learning Neural Networks (DANN), Genetic Optimized Neural Networks (GONN) and Adaptive Neural Networks Algorithm (ADNN). BORNN have shown maximum accuracy and Roc value (98.1% and 0.92 respectively), and minimum error values (MSE = 0.189, MAE = 0.143). The output of the proposed integrated prediction model is fed into a dental robot who proceeds with the treatment process with high accuracy and minimum delay. The proposed prediction model was implemented using a big data analytics tool called Apache SAMOA and experimental results showed its correctness and effectiveness.

Ecossistema de inovação em plataformas de aplicativos

As plataformas de aplicativos fazem parte de ecossistemas de inovação, onde as interações entre usuários finais e desenvolvedores autorregulam o crescimento do próprio ecossistema. Uma das informações mais importantes para esse processo são as avaliações dos usuários. Com o intuito de analisar o impacto dessas informações, este artigo utiliza uma regressão por mínimos quadrados ordinários (MQO) para o ano de 2016, com base em 19 variáveis para 60 países. É mensurado o grau de exigência do mercado consumidor, utilizando um novo indicador baseado nas avaliações dos usuários finais para verificar a consistência de relações entre qualidade da demanda (mensurada pelo novo indicador) e o desempenho inovador de diferentes países nesse segmento. Os resultados evidenciam a robustez e as novas possibilidades de pesquisa que surgem, dadas as características positivas apresentadas pelo novo indicador construído através da utilização de ferramentas de big data e data analytics. Esse indicador mostra que a qualidade da demanda apoia inovações no segmento produtivo, o que levou à concussão de que obter feedback da demanda mais sofisticada representa um estímulo potencial poderoso ao avanço no desenvolvimento de aplicativos.

Automated Transverse Crack Mapping System with Optical Sensors and Big Data Analytics.

Transverse cracks on bridge decks provide the path for chloride penetration and are the major reason for deck deterioration. For such reasons, collecting information related to the crack widths and spacing of transverse cracks are important. In this study, we focused on developing a data pipeline for automated crack detection using non-contact optical sensors. We developed a data acquisition system that is able to acquire data in a fast and simple way without obstructing traffic. Understanding that GPS is not always available and odometer sensor data can only provide relative positions along the direction of traffic, we focused on providing an alternative localization strategy only using optical sensors. In addition, to improve existing crack detection methods which mostly rely on the low-intensity and localized line-segment characteristics of cracks, we considered the direction and shape of the cracks to make our machine learning approach smarter. The proposed system may serve as a useful inspection tool for big data analytics because the system is easy to deploy and provides multiple properties of cracks. Progression of crack deterioration, if any, both in spatial and temporal scale, can be checked and compared if the system is deployed multiple times.

A framework for selecting analytics tools to improve healthcare big data usefulness in developing countries

Background: In many developing countries including South Africa, there are challenges in understanding how the different networks of patients, diagnoses, and medical personnel are formed, as well as the types of big data that are generated. The challenges include the relationship and interaction that exist between the big data within the various networks. Some of the challenges manifest into different factors such as inaccuracy of data, inconsistency, incompleteness of data, and lack of cohesion. The trajectory of the challenge is the inability to select the most appropriate analytics tools for big data analysis. Objectives: The objective of the study was to propose a solution that can be used to address the challenges in selecting analytics tools, to enhance big data usefulness for the improvement of healthcare services, particularly in developing countries. Method: Literature of within 10 years of publication in the areas of big data, big data analytics and healthcare were gathered and used as data. The analysis of the data followed the hermeneutics approach within the interpretivist paradigm. Results: From the analysis, factors that influence big data and analytics tools’ usefulness were found, based on which a solution (framework) is proposed. The solution is intended to contribute to the works of information systems and technologies (IS/IT) personnel, health practitioners and academics. Conclusion: The relationships that exit between actors, and the actors’ interactions in the process of providing medical services contribute to the sources of big data. Therefore, it is necessary to holistically analyse healthcare big data from both technical and non-technical perspectives.

Digital twins of food process operations: the next step for food process models?

Food process modeling has matured with the development of multiscale, multiphase and multi-physics approaches. More comprehensive numerical tools and software platforms for improving insights and optimizing designs and processes have emerged. In the context of industrial digitalization and the advent of the Internet of Things, the concept of the digital twin has recently emerged as a means for more versatile process operational management. The digital twin is defined as a virtual replica of the real process operation, which is connected to the real world by sensor data and advanced big data analytical tools. While all elements are available for implementing digital twins, with the different types of models playing a central role, it will require a multidisciplinary approach for successful implementation and operation. The first agrofood applications still need to be demonstrated. This paper mainly focusses on the role more physics-based models can play, in addition to data-driven and hybrid models.

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics.

Utilization of text mining as a big data analysis tool for food science and nutrition.

Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.

Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning.

Background: The primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: The study was intended to assess the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: Twenty machine learning algorithms (based on five types of algorithms and four text representation techniques) were trained using a sample of 3559 messages (169,102 words) corresponding to 2268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: The best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables “avoiding the need of a face-to-face visit” and “increased demand” (precision = 0.98 and 0.97, respectively) rather than the variable “type of query” (precision = 0.48). Conclusion: To the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.

Use of big data analysis to investigate the relationship between natural radiation dose rates and cancer incidences in Republic of Korea

In this study, we investigated whether there is a significant relationship between the natural radiation dose rate and the cancer incidences in Korea by using a big data analysis. The natural dose rate data for this analysis were the measurement data obtained from the 171 monitoring posts of the 113 administrative districts in Korea over the 10 years from 2007 to 2016. The relative cancer incidences for this analysis were the difference in the cancer patients per hundred thousand people year-on-year in the administrative districts with the five highest and the five lowest natural gamma dose rates each year over the same period. To analyze the correlation between the two variables, Spearman's rank correlation coefficient between the two rates was derived using R, a well-known big data analysis tool. The analysis showed that Spearman's rank correlation coefficient was more than 0.05 and that the correlation between the two variables was not statistically significant.

Rational design of affinity ligands for bioseparation

Affinity adsorbents have been the cornerstone in protein purification. The selective nature of the molecular recognition interactions established between an affinity ligands and its target provide the basis for efficient capture and isolation of proteins. The plethora of affinity adsorbents available in the market reflects the importance of affinity chromatography in the bioseparation industry. Ligand discovery relies on the implementation of rational design techniques, which provides the foundation for the engineering of novel affinity ligands. The main goal for the design of affinity ligands is to discover or improve functionality, such as increased stability or selectivity. However, the methodologies must adapt to the current needs, namely to the number and diversity of biologicals being developed, and the availability of new tools for big data analysis and artificial intelligence. In this review, we offer an overview on the development of affinity ligands for bioseparation, including the evolution of rational design techniques, dating back to the years of early discovery up to the current and future trends in the field.