Big Data Analytics Tools Research Articles

Initial Implementation of Data Analytics and Audit Process Management

To answer the call for more evidence on the adoption and effectiveness of Big Data Analytics in auditing, this study investigates auditors’ use of data analytic tools in audit-process management, including audit planning, testing, and conclusions. The analysis, which is performed as a qualitative study, is based on twenty-eight semi-structured interviews with Big 4 and non-Big 4 audit professionals in Thailand to gain insights into their experience implementing audit data analytic tools in the initial stage. Findings suggest that auditors primarily use data analytic tools in audit planning and substantive testing. Nevertheless, auditors do not perceive a need to use these tools to test internal controls and conclude audit opinions. In addition, we find that auditors tend to apply audit data analytic tools for anomaly detection and testing management assertions. Overall, auditors perceive the benefits of audit data analytic tools in improving their audit process management. Findings present practical implications for audit firms and audit professionals, including how to initially implement data analytic tools effectively in auditing and as guidelines for regulators on how to develop auditing standards that govern the use of Big Data and data analytic tools. We note some limitations in this study, such as the generalizability of the results, auditors’ personal biases, and the different tools and techniques used by each audit firm.

A Deep Learning Approach for Classification of Medicare Beneficiaries Based on Gender and being Affected with Cancer

With the advent of the Third computing platform of Social Mobility Analytics and Cloud (SMAC), data is getting generated in huge amounts. This huge amount of data is collected for domain-specific information to process them to get required domain-specific information as in real-time health analytics, financial frauds, real-time automated car driving, vital information of patients undergoing robotic surgery, handling cyber threats etc. This huge data, also known as Big Data, is highly unstructured and imbalanced that is not possible for traditional techniques to handle and process. Advancements in computing power, speedy data storage and convergence of SMAC technologies have also contributed to the swift acceptance of the technology. This led to innovative analytical techniques that are data as well as computation intensive. One such technique is Deep Learning which originated from the artificial neural network and found its use in handling many real-life problems involving multidimensional features. The advantage of Feature Learning or Representational Learning makes Deep Learning a wonderful tool for big data analytics. The previous level of hierarchy transfers the feature learning to the next levels and thus complex features are learned through the learning of simpler features at different levels of abstraction. For efficient learning of these features, tuning of hyper-parameters is a mandatory step. The current work incorporates Grid Search for classification to find the best classifier for the classification of Medicare beneficiaries based on two scenarios. The first Scenario is beneficiaries who are affected by cancer and the Second Scenario is where Medicare beneficiaries are provided Gender wise (being a Female beneficiary). By experimenting using these algorithms at 10-fold cross-validation, the best results were achieved in the sensitivity of 99.17 %, Specificity of 97.68 % and accuracy of 98.8 % with Deep Learning Neural Network with Dropout for First Scenario and achieved the best results in the sensitivity of 82.97 %, Specificity of 68.71 % and accuracy of 75.05 % with Random Forest for Second Scenario.

Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data

A flood is a significant damaging natural calamity that causes loss of life and property. Earlier work on the construction of flood prediction models intended to reduce risks, suggest policies, reduce mortality, and limit property damage caused by floods. The massive amount of data generated by social media platforms such as Twitter opens the door to flood analysis. Because of the real-time nature of Twitter data, some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue strategy. However, due to the shorter duration of Tweets, it is difficult to construct a perfect prediction model for determining flood. Machine learning (ML) and deep learning (DL) approaches can be used to statistically develop flood prediction models. At the same time, the vast amount of Tweets necessitates the use of a big data analytics (BDA) tool for flood prediction. In this regard, this work provides an optimal deep learning-based flood forecasting model with big data analytics (ODLFF-BDA) based on Twitter data. The suggested ODLFF-BDA technique intends to anticipate the existence of floods using tweets in a big data setting. The ODLFF-BDA technique comprises data pre-processing to convert the input tweets into a usable format. In addition, a Bidirectional Encoder Representations from Transformers (BERT) model is used to generate emotive contextual embedding from tweets. Furthermore, a gated recurrent unit (GRU) with a Multilayer Convolutional Neural Network (MLCNN) is used to extract local data and predict the flood. Finally, an Equilibrium Optimizer (EO) is used to fine-tune the hyperparameters of the GRU and MLCNN models in order to increase prediction performance. The memory usage is pull down lesser than 3.5 MB, if its compared with the other algorithm techniques. The ODLFF-BDA technique’s performance was validated using a benchmark Kaggle dataset, and the findings showed that it outperformed other recent approaches significantly.

Big data as a means of management by construction, or “Hypernudge”

This study makes the case that the analytical phenomena now known as “Big Data” may be viewed as a kind of “model” management by drawing on regulated studies. This report concentrated on algebraic choice strategies, even if Big Data life choice methods may take the shape of bot’s systems. These methods are being applied to mould the factual choice scope in which actual decision takes place in order to special attention and outcome in the locations desired by the “choice builder” by emphasizing commonalities respectively data items that wouldn't otherwise be demonstrable. These methods are a “soft” kind of construction control because they depend on the use of “wink,” a specific type of decision-making that modifies individual choices in a consistent pattern without denying them any alternatives or significantly altering their market benefits. Although, unlike the permanent Moves that were snapped up. Due to their integrated, constantly refreshed, variable, and ubiquitous character, Big Data analytic moves are very strong and impactful, such as putting the salad in front of ravioli to promote a healthy lifestyle. I use a liberal, constitutional protections approach to critiquing these methods, comparing liberal different theories with chosen findings from core state powers and technological and scientific technologies (STS) on the other. I contend that relying on individual notice and approval does not adequately address concerns well about the authenticity of these processes. I also discuss the time in many cases for freedom and human booming if Big Data analytic tools driven by promotional conscience continue to develop unaddressed by lawful and productive restraints.

Discrete Emotions Response after Purchase Cosmetics of Millennial Consumers: Evaluation of Satisfaction/Normal/Dissatisfaction Attributes Using Text Mining Techniques

Purpose: This study aimed to analyze the similarities and differences between each area using wordcloud analysis and semantic network analysis, which are text mining techniques. Further, confirmatory factor analysis will be conducted by crawling for word-of-mouth information on attribute reviews as satisfied, normal, or dissatisfied after purchases that are subjectively given by millennial generations.Methods: The R program version 4.1.2 was used as a big data collection and analysis tool, and text mining analysis was performed through preprocessing and stopword processing on the collected data. Further, using LISREL 8.80 we conducted confirmatory factor analysis on these results.Results: Wordcloud analysis revealed that the terms “skin,” “products,” and “skin” ranked first in the evaluation area of “satisfied,” “normal,” and “dissatisfied,” respectively. Additionally, using confirmatory factor analysis, the correlation between the three latent variables of satisfaction, normal, and dissatisfaction was differentiated.Conclusion: The similarities and differences between the domains obtained through wordcloud and semantic network analyses and derived by classifying individual emotional responses of millennial consumers in social media into satisfied, normal, and dissatisfied domains are considered very meaningful. The keywords derived with high centrality in the semantic network for each domain is then refined and introduced as an observation variable for confirmatory factor analysis in accordance with the purpose of the study; this is helpful in research development for causal analysis in the future.

Application of data mining technologies to find correspondences of development patterns in large arrays of web data based on Big Data analysis tools

A number of modern scientific studies interpret corporate strategy as a certain way to implement a specific action plan for an enterprise through the development of management decisions in a highly competitive environment with a limited set of resources. Management is a chain of various solutions, as well as tools that enable the practical implementation of these solutions. The relevance of the topic studied in the article is associated with the development of a global trend for digital technologies and solutions that have affected almost all aspects of society. The digitalisation of management activities directly affects the formation of an enterprise management strategy: in this way, automation of administration allows to systematically transfer relations between various groups of business entities, businesses and consumers to a new unique environment created in digital form, which ultimately allows to qualitatively increase the level of development and decision-making methods effectiveness in the field of management. The article defines the practical areas of using data mining technology in the context of the development of the digital economy. The key trends in the development of intelligent data volume technology are highlighted. The authors consider a variant of the architecture that allows optimizing web data processing.

Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method

Abstract In this paper we investigate the comments from the AlgoRythmics YouTube channel using the Comment Term Frequency Comparison social media analytics method. Comment Term Frequency Comparison can be a useful tool to understand how a social media platform, such as a Youtube channel is being discussed by users and to identify opportunities to engage with the audience. Understanding viewer opinions and reactions to a video, identifying trends and patterns in the way people are discussing a particular topic, and measuring the effectiveness of a video in achieving its intended goals is one of the most important points of view for a channel to develop. Youtube comment analytics can be a valuable tool looking to understand how the AlgoRythmics channel videos are being received by viewers and to identify opportunities for improvement. Our study focuses on the importance of user feedback based on ten algorithm visualization videos from the AlgoRythmics channel. In order to find evidence how our channel works and new ideas to improve we used the so-called comment term frequency comparison social media analytics method to investigate the main characteristics of user feedback. We analyzed the comments using both Youtube Studio Analytics and Mozdeh Big Data Analysis tool.

HOW BIG DATA WILL BE AN ADDED VALUE TO SCM?

It is the era of digital information technology where almost everything is going smart. Thus, organizations move towards digitalization that cause the emergent of Big Data. Analyzing big data is the big challenge today. Being smart puts the world under a big challenge to adapt, change, and upgrade systems to analyze the Big Data using the high technology. Challenges are growing with the market and appears in different forms. Many of these challenges can be hard or difficult to handle on your own if you are a small to medium size business without the help of supply chain management system. The main objective of this paper is to contribute and examine these research questions: What are the Big Data Analytical tools used in SCM? In addition to the Impact of Analyzing Big Data on Supply Chain Management? The methodology used was a systematic review over the existing literature including Big Data, supply chain, SCM, and the impact of BD analytical tools on SCM. Data collected and unsystematically interpreted and the findings summarized in a subjective way that describes and discusses the literature from a contextual or theoretical point of view. Big data can tremendously affect the supply chain units and can add values to the overall supply chain operations by improving the processes to be more effective and efficient based on the analysis results. Big data analytics become a core differentiation factor for any organization that acquire it in the last few years.

BIGDATA ANALYSIS ON METHODS AND TOOLS

As the name of denote, huge information suggests that an excellent assortment of knowledge that can’t be continue while not ancient computing approach. To reason the information, it would like tool and technique. Having information larger consequently needs totally different approaches, techniques, tools & architectures to manage the information in an exceedingly higher means. huge information technologies give additional authentic analysis that facilitate in deciding. To manage and huge amount volume of structured semi-structured and unstructured information you'd need an that it desires so as to perform which will be assured, privacy and shield the information. There are varied tools and technologies within the market from totally different vendors IBM, Amazon, Microsoft, etc., to pick up the huge information. the key challenges with huge information are Capturing information, Storing information, Searching, Curation, Sharing, Transfer, Analysis, Presentation, to satisfy the on top of challenges, organizations commonly take the assistance of venture servers. Multiple methodologies (technologies/methods) supported sure parameters to draw that of them is absolute best (optimal) or what else has to be done, to own AN optimum resolution. because the information is being created and collected at a awfully high speed with diversity additionally to that, process has become a unexciting task. Whereas the actual fact remains that, by process of this information we'll uncover gold from these Brobdingnagian mountains however if it’s left untreated it'll become Everest’s of refuse. Since the start of term huge information several strategies and or technologies ar gift that ar being employed to procedure these information mountains. These technologies have their own space of interest because of that there are plain disadvantage and mode of operations between them. when analysis we have a tendency to establish that common to them is low activity and therefore the information management. Once the information Management to produce the Cloud technology. Keywords: Bigdata, bigdata tools and methods.

텍스트 마이닝을 활용한 국내 체육철학의 연구동향 분석 : 한국체육철학회지를 중심으로

This study aims to find the direction of Korean sports philosophy by analyzing the research included in the Journal of Korean Philosophic Society for Sport and Dance(JKPSSD), analyzing the changes and trends made in the development of the KPSSD, and providing basic data to predict the future. Text mining was conducted using Textom, a big data analysis tool, for 1308 papers included by the first half of 2022, and the following results were obtained. First, the KPSSD started with its first issue in 1992 and until the first half of 2022, and a total of 1308 studies were published. Second, the top 10 frequent words were sports, research, Kim, D.K., Taekwondo, consideration, Korea, center, meaning, search, and Kwon, O. R.. Third, as a result of the N-gram analysis explaining the relationship of each word, it was found that the relationship between criticism and consideration, modern and significance, physical education and sports, Kim, H. S. and Kim, D. K., Kim, D. K., and sports was high. The KPSSD has played a major role in the development of Korea

AI-based big data analytics model for medical applications

Today, people's daily lives depended on their mobile phones, facilitating many beneficial tasks. Mobile computing and cloud computing combine cloud-based mobile computing to improve their features and overcoming their disadvantages such as memory space, processor, and longer battery life. By combining the four "V's" of volume, variety, velocity and veracity, Big Data analytics tools allow the utility to be extracted through knowledge. This paper explores the development of interconnected healthcare systems and the contribution of mobile cloud applications and big data analysis. With the implementation of cloud computing in the healthcare system, inspiration and development of interconnected health - software and care services are given. Big Data services for the healthcare sector on a cloudlet-based mobile cloud computing platform. Big data analysis methods, equipment, and solutions are examined. We have concluded the development of interconnected medical systems using Big Data and mobile cloud computing.

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

Using the Baidu index to understand Chinese interest in thyroid related diseases

Common thyroid diseases are hyperthyroidism, hypothyroidism, thyroiditis, thyroid tumor and so on. Baidu is currently the most widely used online search tool in China, has developed an internet search trends collection and analysis tool called the Baidu Index. The aim of the present study was to understand the trend and characteristics of public’s online attention to thyroid diseases, and to explore the value of Baidu Index in monitoring online retrieval behavior of thyroid-related information. Taking the period from January 1, 2011 to December 31, 2019 as the time range into consideration, we used the big data analysis tool of Baidu Index and took “thyroid nodules”, “thyroid cancer”, “thyroiditis” “hyperthyroidism” and “hypothyroidism” as the keywords, the data of “search index” and “media index” were recorded on a weekly basis, and all information were aggregated into quarterly and annual to generate the final data which was carried out for secondary analysis. Pearson correlation analysis was used to analyze the correlation between the search index of keywords and the year. One-way Analysis of Variance was used to analyze the differences between search index and media index. Among the five keywords, thyroid nodule search index had the highest growth rate (640%), followed by thyroid cancer (298%). The media’s attention to thyroid diseases had been declining year by year. Unlike the public’s attention, the media index of hyperthyroidism was significantly higher than other keywords. Over the past nine years, the public's attention to thyroid-related diseases has been increasing gradually. Baidu Index is an effective tool to track the health information query behavior of Chinese internet users, which can provide a cost-effective supplement to traditional monitoring system.

The Challenge and Approach of Improving the Big Data Analysis Ability of Logistics Professionals

The development of the logistics industry has driven the explosive growth of logistics data. However, the market lacks skilled professionals to carry out source management, processing, analysis and mining of logistics data. Under the growing talent gap, the cultivation of big data talents has become a global realistic demand. This paper takes the logistics professionals trained in colleges and universities as the research object. We analyzes the difficulties of improving the big data analysis ability of logistics professionals from three aspects. A promotion approach for the big data analysis ability is proposed. The research believes that the training of big data thinking, big data analysis tools and cooperation between universities and enterprise should become the main means for colleges and universities to promote the high quality and sustainable development of the logistics education in the era of big data.

Analysis of Text Mining Keyword Issues for Defense Drone on Social Network

Research on weapon systems has been conducted, and defense drones have been developed. However, few analyses that identify the needs of the general public have been conducted. The purpose of this study is to analyze the issues of interest in social networks regarding defense drones and extract keywords for activation. In this study, big data analysis tools such as Textom, Ucinet6, and NetDraw were used. The results of the keyword frequency analysis, centrality analysis, and CONCOR analysis showed that public information regarding social networks expressed keywords in defense drones, such as Defense Daily, News, Enterprise, Defense Science Research Institute, University, Hydrogen Battery, Army, Air Force, Urban Transportation, Fire, Regional, Enterprise, and Research. It is necessary to revitalize media publicity in developing cooperative technologies by companies, universities, the National Defense Research Institute, urban transportation, and firefighting, using army and air force drones, and developing future hydrogen batteries. In addition, issues of interest were observed for contents, such as applications in the United States, Israel, North Korea, Iran, and Afghanistan, and fostering university education. There is a need to present a case for the actual military use of defense drones and train the department of defense drones at universities.

Plant health index as an anomaly detection tool for oil refinery processes

Early detection of significant abnormal changes is highly desirable for oil refinery processes, which consist of sophisticated unit operations handling hazardous and flammable inventories and operating at high temperature and pressure. Close monitoring and anomaly detection are vital for avoiding major accidents and losses and enable intervention before failure occurrence. A new big data analytics tool called Plant Health Index (PHI) is proposed in this work. PHI is a statistical anomaly detection software that trains its model using online normal plant operation, then uses statistical analytics to detect anomalies. For detecting the anomalies, a combined method of multivariate analysis of residuals and nonparametric models of the process is employed. The methodology provides a structured representation of the plant variables to ease the detection of problems along with the detection of operation changes of the system. The PHI system has been tested on a hydrotreating units in a refinery, which consists of catalytic reactors and separators. The current implementation tagged 170 process variables and proved effective in capturing the normal operational conditions of the plant. When placed online, PHI was able of detecting anomalies that are difficult to detect using the control system and before being detected by the alarm system.

Towards an Inclusive Society: Review of Google Trends Data of User Interest in the Deaf Comparing to the Blind and the Disabled

The COVID-19 pandemic has brought great difficulties for the deaf, the blind and people with disabilities in general, in addition to the difficulties of everyday life. This study aims to examine the public interest in the social integration of the deaf and hard of hearing community during the pandemic and the pre-pandemic period in the context of an inclusive society. The empirical study is based on the analysis of statistical data provided by Google Trends, which is a big data analysis, relative measurement and information visualisation tool that allowed to get insight on gaps and inconstancy of information seekers’ interest within categories of science, people and society, law and government, and health. The study encourages researchers and social policy makers to pay extra attention to the social inclusion of the deaf, which, according to the research data, worsened during the pandemic more than other groups of the disabled.

Comparing Worldwide, National, and Independent Notifications about Adverse Drug Reactions Due to COVID-19 Vaccines

The rapid development of effective vaccines against COVID-19 is an extraordinary achievement. However, no medical product can ever be considered risk-free. Several countries have a pharmacovigilance system that detects, assesses, understands, and prevents possible adverse effects of a drug. To benefit from such huge data sources, specialists and researchers need advanced big data analysis tools able to extract value and find valuable insights. This paper defines a general framework for a pharmaceutical data analysis application that provides a predefined (but extensible) set of functions for each data processing step (i.e., data collection, filtering, enriching, analysis, and visualization). As a case study, we present here an analysis of the potential side effects observed following the administration of the COVID-19 vaccines. The experimental evaluation shows that: (i) most adverse events can be classified as non-serious and concern muscle/joint pain, chills and nausea, headache, and fatigue; (ii) the notification rate is higher in the age group 20–39 years and decreases in older age groups and in very young people.

Does Big Data Drive Innovation In E-Commerce: A Global Perspective?

Objective: Literature indicates big data is a competitive edge, which boasts a firm’s overall performance. With the rise of big data (BD), e-commerce firms are using the tools to engage more with customers, offer better products, and innovate more to gain a competitive advantage. Nevertheless, past empirical studies have shown conflicting results. Design: Building on the capital-based perspective and the firm’s inertia concept, we created a model to explore how BD and BD analytics capability impact innovation results in e-commerce businesses. We carried out a two-year empirical investigation project to secure empirical data on 1703 data-driven innovation tasks from USA and Asia. Findings: We showed that there is a tradeoff between BD and BD analytics capability, in which the optimum balance of BD depends on the amount of BD analytics ability. BD analytics ability exerts a good moderating impact, that is, the better this capability is, the higher the effect of BD on gross margin and sales growth. For U.S. innovation tasks, BD has an inverted U-shaped relationship with sales innovation. For Asian innovation tasks, when major data capital is minimal, promoting big data analytics capability improves sales innovation and disgusting margin up to a specific point. Policy Implications: Establishing BD analytics capability over that time could prevent innovation efficiency. Our findings offer guidance to e-commerce firms on producing strategic choices about source allocations for BD and BD analytics ability. Originality: A limited research has been carried out to show the impact of using BD analytics tools to drive innovation. This is one of the first articles that dive into using BD to foster innovation in the e-commerce business.

Sustainable Smart Industry: A Secure and Energy Efficient Consensus Mechanism for Artificial Intelligence Enabled Industrial Internet of Things.

In recent years, the Internet of Things (IoT) has been industrializing in various real-world applications, including smart industry and smart grids, to make human existence more reliable. An overwhelming volume of sensing data is produced from numerous sensor devices as the Industrial IoT (IIoT) becomes more industrialized. Artificial Intelligence (AI) plays a vital part in big data analyses as a powerful analytic tool that provides flexible and reliable information insights in real-time. However, there are some difficulties in designing and developing a useful big data analysis tool using machine learning, such as a centralized approach, security, privacy, resource limitations, and a lack of sufficient training data. On the other hand, Blockchain promotes a decentralized architecture for IIoT applications. It encourages the secure data exchange and resources among the various nodes of the IoT network, removing centralized control and overcoming the industry's current challenges. Our proposed approach goal is to design and implement a consensus mechanism that incorporates Blockchain and AI to allow successful big data analysis. This work presents an improved Delegated Proof of Stake (DPoS) algorithm-based IIoT network that combines Blockchain and AI for real-time data transmission. To accelerate IIoT block generation, nodes use an improved DPoS to reach a consensus for selecting delegates and store block information in the trading node. The proposed approach is evaluated regarding energy consumption and transaction efficiency compared with the exciting consensus mechanism. The evaluation results reveal that the proposed consensus algorithm reduces energy consumption and addresses current security issues.