Interest In Big Data Research Articles

Blockchain for Big Data: Approaches, Opportunities and Future Directions

Abstract: The last several years have seen a significant increase in interest in big data across a range of scientific and engineering fields. Despite having several benefits and applications, big data still has some difficulties that must be overcome for a higher level of service, such as big data analytics, big data management, and big data privacy and security. Big data services and apps stand to greatly benefit from blockchain decentralisation and security features. In this article, we present an overview of blockchain for big data with an emphasis on current methods, possibilities, and upcoming trends. We begin by providing a succinct explanation of big data, blockchain, and the purpose of their integration. After that, we look at different types of blockchain assistance for big data, such as blockchain for security in big data collection, data privacy protection, storage, and collection. Next, we examine the latest work on the utilization of blockchain applications for big data across different industries, including smart grid apps and applications, smart city applications, and smart healthcare applications. A few illustrative blockchain-big data initiatives are given and discussed for a good understanding. Finally, difficulties and potential directions are examined to advance research in an exciting field.

Big data in Antarctic sciences – current status, gaps, and future perspectives

Abstract. This paper was initiated by a multidisciplinary Topic Workshop in the frame of the Deutsche Forschungsgemeinschaft Priority Program 1158 “Antarctic Research with Comparative Investigations in Arctic Ice Areas”, and hence it represents only the national view without claiming to be complete but is intended to provide awareness and suggestions for the current discussion on so-called big data in many scientific fields. The importance of the polar regions and their essential role for the Earth system are both undoubtedly recognized. However, dramatic changes in the climate and environment have been observed first in the Arctic and later in Antarctica over the past few decades. While important data have been collected and observation networks have been built in Antarctica and the Southern Ocean, this is a relatively data-scarce region due to the challenges of remote data acquisition, expensive labor, and harsh environmental conditions. There are many approaches crossing multiple scientific disciplines to better understand Antarctic processes; to evaluate ongoing climatic and environmental changes and their manifold ecological, physical, chemical, and geological consequences; and to make (improved) predictions. Together, these approaches generate very large, multivariate data sets, which can be broadly classified as “Antarctic big data”. For these large data sets, there is a pressing need for improved data acquisition, curation, integration, service, and application to support fundamental scientific research. Based on deficiencies in crossing disciplines and to attract further interest in big data in Antarctic sciences, this article will (i) describe and evaluate the current status of big data in various Antarctic-related scientific disciplines, (ii) identify current gaps, (iii) and provide solutions to fill these gaps.

Business intelligence for Industry 4.0: predictive models for retail and distribution

Purpose Textile and contemporary apparel manufacturers are adopting and integrating cutting-edge technologies to reduce their impact on the environment and gain an advantage in the marketplace. Most previous studies have ignored business intelligence systems (BIS), notably in the textile and apparel industry (T&A), in favor of looking at the larger picture of how big data would affect retail and distribution in a company. This is especially true for the T&As.Design/methodology/approach The authors report that they conducted 14 semi-structured interviews with 12 international luxury tourism service providers. In this case, researchers use snowball features and systematic techniques to select participants. A qualitative content analysis strategy is used to capture the focus of the interviews.Findings Problems with T&A company sustainability, opportunities to increase value creation via use of industry-leading business intelligence (BI) solutions and perceived roadblocks to BIS adoption were all found by the poll. Garment retail and distribution sector has benefited greatly from the increased use of Industry 4.0 technologies, especially those that provide better BI solutions. Determine the extent to which industry participation slows down or speeds up the process. The Company Information System (BIS) will help convince non-tech-savvy business owners of the financial, economic and environmental benefits of adopting certain technologies developed as part of the industry 4.0 movement.Research limitations/implications The authors of this research claim theirs is one of the first to investigate what variables affect the uptake of BIS, ultimately hoping to find out how BIS may be used by T&A businesses to tackle environmental issues through the use of Industry 4.0 technologies. The purpose of this study was to see whether BIS might aid T&A firms with their sustainability issues.Practical implications In the last several years, there has been a meteoric rise in interest in big data and business analytics among firms and educational institutions alike. This paper tries to introduce readers to the concept of business analytics in a way that is both academic and accessible, considering both the present and future of the field. This paper begins with a quick introduction, followed by a summary of the three distinct forms of predictive modeling discussed.Originality/value In an effort to help aspiring analytics professionals, they have identified, categorized and evaluated the nine distinct players that are now active in the analytics market. Following this, they will provide a high-level summary of the many different research projects currently being worked on by their group.

Development and Utilization of Drug Lifecycle Data in the United States, Japan, and Europe

Objectives: With the growing interest in big data, regulatory agencies in United States, Japan, and Europe are showing active movements to use RWD and RWE to manage drug safety and development activation. Along with the global movements, it is necessary to establish a basic system for utilizing RWD based drug lifecycle data for drug safety management in Korea. The purpose of this study is to review and find implication of the collection and utilization of drug lifecycle data in the U.S., Japan, and Europe.Methods: In order to investigate the current status and contents of each country’s system, we searched the websites of each country’s regulatory agency and related agencies, and collected and investigated related laws, papers, and reports. We compared and analyzed focusing on solving regulatory problems that may occur in the drug lifecycle or exploring the safety and potential effects of drugs in drug lifecycle phases.Results: Based on FDA Sentinel 5-year plan and MID-NET, the U.S. and Japan are accelerating the generation of real data and real evidence for regulatory decision-making in post-marketing safety management. In Europe, various clinical field-specific projects have been undertaken to assess the effectiveness and safety of drugs due to disease. Various research cases using drug lifecycle data collected from drug safety management systems in each country have been represented.Conclusions: By identifying drug safety management systems and cases of drug lifecycle data utilization in various countries, we expect to serve as a stepping stone for discussions on the direction and utilization plan to efficiently develop drug safety management systems in Korea.

Big Data Analytics Applications and challenges in Real Time

The phrase "Big Data" was used to describe a large amount of data that cannot be managed using typical data handling methods or procedures. In numerous industries, including agro, banking, data mining, education, chemistry, finance, cloud computing, marketing, and health care stocks, the field of big data is indispensable. Big data analytics is a technique for analysing large amounts of data to find hidden patterns, mysterious relationships, and other crucial information that can be used to make better decisions. Because of its rapid development and wide range of applications, there has been a steadily growing interest in big data. Key words: Big data, Analytics, Healthcare, weather-forecast, Education, Government, Hdfs

Digital Transformation and Big Data

Today, the Russian Federation pays special attention to the digital transformation of the country as a whole and shows a separate interest in big data in particular, which is confirmed by a number of analyzed documents and the current situation in the subject area. The research work proposed for reading is devoted to the analysis of big data, one of the areas of use of which is the state, or rather the work of federal and regional executive authorities. The main value of big data from the position of the state is seen in the possibility of working on volumes of heterogeneous information in order to increase efficiency in making managerial decisions on a wide range of issues. The main purpose of the study is to disseminate the experience of big data analysis, which is used in the work of the Ministry of Economic Development of Primorsky Krai. To do this, it is proposed to turn to the theoretical aspects of big data, find out the main chain of work on them, and also pay attention to domestic and foreign experience in using data in some areas of knowledge. The practical part of the study is a description of the experience of the Ministry of Economic Development of the PC, which is directly related to the digital development of the region and carries out a certain list of works with heterogeneous data. As an example, attention is drawn to a proven approach to the analysis of a large set of open data characterizing the work and development of small and medium-sized businesses in the Primorsky Territory, posted on the website of the Federal Tax Service of the Russian Federation. In conclusion, it is emphasized that the approach proposed in the study for working with data can be adapted to similar needs in other federal and regional authorities

Assessing the Big Data Analytics Readiness based on Technology-Organization-Environment (TOE) Framework of Malaysian Libraries: Descriptive Analysis

The growing interest in big data is mostly due to its enormous potential for revealing previously unknown patterns or profiles that aid organisations in making critical service decisions. Following other sectors' adoption of big data, the information services sector, such as libraries, has recently jumped on board. Although libraries face challenges in adopting new service models to assist with the transformation of data into information, big data has been identified as one of the potential mechanisms for enhancing service delivery. Libraries must increase their technology knowledge and capability in order to capitalise on emerging opportunities in the developing field of library data analytics, which provides fresh insights into established service models. Regardless of the dedication of libraries and information services to promote big data, the readiness of libraries, as well as their staffs and infrastructure, such as technology, is critical to the effective deployment of big data. Inadequate applications of big data prior to maturity might backfire and result in long-lasting operational and institutional mutilation. Thus, this study used a quantitative approach to investigate the readiness (Technology-Organization-Environment readiness) of Malaysian libraries for big data potentials. The findings indicate that the dimensions of Malaysian libraries' readiness for BDAs are primarily high-level (in nature, encompassing technological, organizational, and environmental dimensions. The study included academic libraries, public libraries, and special libraries, and utilised a survey research methodology involving 370 libraries. The findings of the study are expected to provide important insight into the factors affecting the preparation of Malaysian libraries for big data potentials and the projected effect of increased/lower change readiness in Malaysian libraries.

BIG DATA IN THE MARKETING EDUCATION PROGRAMS: EDUCATORS’ AND STUDENTS’ VIEWS AND ATTITUDES

ABSTRACT This research explores the perspectives of marketing students and educators about the Big Data courses in marketing education programs. It also examines drivers that predict interest on the part of marketing students in taking Big Data courses. Data was collected through interviews with 20 marketing educators, and a survey was completed by 480 marketing students. Results of this research show how marketing educators can introduce Big Data concepts in their marketing classes and incorporate new teaching techniques. The results of our first study show that ability factors, affective factors, and interest in big data have an impact on marketing students’ attitudes toward big data courses. Nevertheless, our analysis shows that the students’ competitiveness traits and the academic value of big data do not have an impact on marketing students’ attitudes toward big data courses. Our second study emphasizes the importance of integrating big data courses into marketing education programs. It also lists the requirements for teaching big data in marketing programs, including the skills, qualifications, and experience of big data instructors. Collectively, this research provides insights associated with the growing desire to upskill in big data by marketers and the belief that these skills will be necessary for the future.

Foresights for big data across industries

Purpose The purpose of this study is to explore the potential and growth of big data across several industries between 2016 and 2020. This study aims to analyze the behavior of interest in big data within the community and to identify areas with the greatest potential for future big data adoption. Design/methodology/approach This research uses Google Trends to characterize the community’s interest in big data. Community interest is measured on a scale of 0–100 from weekly observations over the past five years. A total of 16 industries were considered to explore the relative interest in big data for each industry. Findings The findings revealed that big data has been of high interest to the community over the past five years, particularly in the manufacturing, computers and electronics industries. However, over the 2020s the interest in the theme decreased by more than 15%, especially in the areas where big data typically had the greatest potential interest. In contrast, areas with less potential interest in big data such as real estate, sport and travel have registered an average growth of less than 10%. Originality/value To the best of the author’s knowledge, this study is original in complementing the traditional survey approaches launched among the business communities to discover the potential of big data in specific industries. The knowledge of big data growth potential is relevant for players in the field to identify saturation and emerging opportunities for big data adoption.

Big Data in Asian Central Banks

The present paper reviews the use of big data in Asian central banks, leveraging on a survey conducted by the Irving Fischer Committee on Central Bank Statistics (IFC) of the Bank for International Settlements. It reveals four main insights. First, Asian central banks define big data in an encompassing way. Second, they show higher interest in big data, including at the senior policy level. Third, big data already supports a wide range of tasks. Fourth, big data poses new challenges and increases the need for international policy cooperation, especially to make use of payments data and promote innovative technological solutions.

Big data driven order-up-to level model: Application of machine learning

Data driven optimisation has become one of the research frontiers in operations management and operations research. Likewise, the recent academic interest in big data has created a desire for big data driven operations research. A new data driven methodology, which employs the empirical risk minimisation (ERM) principle, has recently been introduced in the inventory management literature. It has been used to formulate data driven inventory models which can take multiple features into account and do not need classical distributional assumptions. However, the research on big data driven inventory models is currently confined to the newsvendor model. In this paper, we aim to generalise the previous results on the big data driven newsvendor model and to expand the research by solving a big data driven dynamic order-up-to level inventory model. We show how the ERM methodology is employed to formulate a big data driven order-up-to level inventory model and design a machine learning algorithm to solve the model. The performance of our big data driven inventory model and solution algorithm is demonstrated by an experimental study based on real business data. The numerical results show that our integrated big data driven model generates up to 60% cost savings compared to the best performing univariate benchmark model, and up to 6.37% cost savings compared to the best performing big data driven benchmark model.

The three legal dimensions of China’s big data governance

Big data governance has emerged as an important area of focus in recent times. In this article, we study the state of big data governance in China, discuss the balance of interests in big data, and seek to develop the theory of big data governance in law. In the past years, some academic achievements have been made in big data governance theories, personal data protection, and data property rights protection based on existing research related to big data governance. However, conflicts of interest in big data have been under-studied and under-analyzed. In this article, we conduct an in-depth study of the state of big data governance in China and develop a theoretical framework for constructing multi-subject governance based on the interest balancing approach, including the allocation of personal data rights and the rights of a data controller, to resolve the conflicts of interests in big data governance in the areas of private law, public law, and industry self-governance.

Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network

Recommendation systems are information filtering tools that present items to users based on their preferences and behavior, for example, suggestions about scientific papers or music a user might like. Based on what we said and with the development of computer science that has started to take an interest in big data and how it is used to discover user interest, we have found a lot of research going on in the area of recommendation and there are powerful systems available. In the unsupervised learning domain, this paper introduces a novel method for creating a hybrid recommender framework that combines Collaborative Filtering with Content Based Approach and Self-Organizing Map neural network technique. By testing our system on a subset of the Movies Database, we demonstrate that our method outperforms state-of-the-art methods in terms of accuracy and precision, as well as improving the efficiency of the traditional Collaborative Filtering methodology.

189 Fundamentals, Common Mistakes, and Graduate Education in Statistics

Abstract Experimental design and statistical data analyses are fundamental components of animal science research. Proper design of experiments and adequate sampling permits testing hypotheses raised by researchers and sets the stage for collecting required data and subsequent statistical analysis. When designing experiments, researchers should respect rules of randomization of treatments to avoid statistical bias and permit proper inference to be drawn. Use of sample sizes that result in adequate statistical power to identify the hypothesized differences among factor levels of interest is key and should be driven by formal processes determining such. Best practices for data collection should be performed to obtain high quality data by reducing collection (e.g., mislabeling, improper technique) and measurement errors. With sound data, appropriate and optimal statistical methods should be used to generate valid results. The statistical method deployed should be chosen based on assumptions about residuals (e.g., normality, correlation, and homogeneity) and on the type of data (e.g., quantitative continuous or categorical). The appropriate statistical model used should also be consistent with the experimental design to validate the respective test statistics. The science of statistics is changing rapidly. With the development of high-throughput technologies, the generation of large datasets, high performance and sophisticated models and the interest in Big Data, the training of animal science graduate students in data management and rigorous statistical analyses is more important than ever. In order to meet the demands of current trends, animal science graduate students must be trained in several complex statistical and computational skills to meet the challenges imposed by these complicated, sophisticated and nuanced analytical methods. The livestock production sector will benefit from improved training, use of advanced and appropriate experimental designs, and collection and analysis of quality data in research.

A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data

The construction industry is recognized as a major cause of environmental pollution, and it is important to quantify and evaluate building energy. As interest in big data has increased over the past 20 years, research using big data is active. However, the links and contents of much literature have not been summarized, and systematic literature studies are insufficient. The objective of this study was a holistic review of building energy efficiency/reduction based on big data. This review study used a holistic analysis approach method framework. As a result of the analysis, China, the Republic of Korea, and the USA had the most published papers, and the simulation and optimization area occupied the highest percentage with 33.33%. Most of the researched literature was papers after 2015, and it was analyzed because many countries introduced environmental policies after the 2015 UN Conference on Climate Change. This study can be helpful in understanding the current research progress to understand the latest trends and to set the direction for further research related to big data.

Use of big data technologies in animal husbandry

The growing interest in big data is driven by several identified factors. Nowadays, humanity uses a large number of equipment that generates multi-format signals and the amount of the generated information is growing exponentially, but the biggest part of this data is unstructured information. In this regard, choosing relevant information and achieving correct interpretations of its flow are becoming more relevant and complex issues. The technologies of Big data allow processing huge volumes and diverse compositions of information that can be regularly updated and located in different sources. The use of theses technologies leads to an increased work efficiency and competitiveness, and develops new knowledge. In this paper, the purpose of the study is to investigate and identify the opportunities of business processes’ digitization in agricultural production.

Big Data for Creating and Capturing Value in the Digitalized Environment: Unpacking the Effects of Volume, Variety, and Veracity on Firm Performance*

Despite significant academic and managerial interest in big data, there is a dearth of research on how big data impacts the long‐term firm performance. Reasons for this gap include a lack of objective indices to measure big data availability and its impact, and the tendency of studies to ignore the costs associated with collecting and analyzing big data, assuming that big data automatically delivers benefits to firms. Focusing on how firms create and capture value from big data about customers, we use the resource‐based view and three dimensions of big data (i.e., volume, variety, and veracity) to understand when the benefits outweigh the costs. Relying on the number of downloads of mobile device applications, we find that volume of big data has a negative effect on firm performance. This result suggests that the “bigness” of big data alone does not ensure value creation for a firm, and could even constitute a “dark side” of big data. Because big data variety—measured as the number of types of information taken per each application—moderates the negative effects of big data volume, simultaneous high values of volume and variety allow firms to create value that positively affects their performance. In addition, high levels of veracity (i.e., a high percentage of employees devoted to big data analysis), are linked to firms benefiting from big data via value capture. These findings shed light on the circumstances in which big data can be beneficial for firms, contributing to a better theoretical understanding of the opportunities and challenges and providing useful indications to managers.

THEORIES AND ANTECEDENTS OF KNOWLEDGE SHARING BEHAVIOUR IN VIRTUAL COMMUNITY: A SYSTEMATIC REVIEW

Virtual Community (VC) is regarded as the best platform for professionals in various fields to share their expertise and knowledge. Since the escalation of web 2.0 and the internet within the last decade and the booming interest in big data and expansion of industry 4.0, VC is deemed as an ideal proxy for practitioners to share and earned instant knowledge that can be implemented within business activities and day to day application. Despite this emerging interest, there has been no comprehensive study on the overall antecedents of KS in VC. Applying for a systematic review, a total of 68 relevant articles that discusses knowledge sharing (KS) via VC are evaluated. Several central themes of theories applied in this field within the literature are discussed on its importance and relevance. Important antecedents are also reviewed on its practicality and implementation in understanding the role of KS in VC. The implication of this review would benefit stakeholders in maintaining the sustainability of VC as the platform for a knowledge-based society.

An empirical investigation on how big data analytics influence China SMEs performance: do product and process innovation matter?

ABSTRACT Globalization and a keen interest in big data have directed the firms to develop and utilize big data analytics (BDA) to bring technological innovation (TI) and enhance firm productivity. Using resource-based view theory (RBVT), we intend to predict TI and SMEs’ performance through BDA. Therefore, we recruited 312 Chinese SMEs’ officials using survey methods. The proposed model and structural associations were examined in AMOS. The findings suggest that BDA (predictive-and-prescriptive) is positively related to TI (product-and-process) and SMEs’ performance. Moreover, TI (product-and-process) mediates the relationship between BDA and SMEs performance. Finally, the study discussion and implications are recorded.

An Analysis of Association Rule Mining Algorithm Techniques Geographical Point of Interest in Big Data

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.