Big Data Research Research Articles

Big Data Management Readiness among Public Universities in Malaysia

Big data is one new technology that businesses use to achieve and maintain high competitive advantage in current Internet and Cloud-based business environment. Big data technologies are presently growing in higher education institutions, especially in the United States, the United Kingdom and Europe. However, the use of big data technologies in Malaysia is relatively small and in its infancy. In Malaysia, though big data approach has been received well in banking and marketing sectors; its embrace is rather insignificant in the education segment. Hence, it is vital for big data research to establish its footing on this essential area of future human capital development for the country. Based on the model of Unified Theory of Acceptance and Use of Technology (UTAUT), this research analyzes the factors that may affects the readiness of big data management, as well as the intention and behaviour to adopt the technology of big data in Malaysian higher education institutions. Consequently, the institutions can use the findings as a guide to utilize big data technology for better innovative data- driven teaching, learning orientation and decision-making.

Development and applications of GIS-based spatial analysis in environmental geochemistry in the big data era.

The research of environmental geochemistry entered the big data era. Environmental big data is a kind of new method and thought, which brings both opportunities and challenges to GIS-based spatial analysis in geochemical studies. However, big data research in environmental geochemistry is still in its preliminary stage, and what practical problems can be solved still remain unclear. This short review paper briefly discusses the main problems and solutions of spatial analysis related to the big data in environmental geochemistry, with a focus on the development and applications of conventional GIS-based approaches as well as advanced spatial machine learning techniques. The topics discussed include probability distribution and data transformation, spatial structures and patterns, correlation and spatial relationships, data visualisation, spatial prediction, background and threshold, hot spots and spatial outliers as well as distinction of natural and anthropogenic factors. It is highlighted that the integration of spatial analysis on the GIS platform provides effective solutions to revealing the hidden spatial patterns and spatially varying relationships in environmental geochemistry, demonstrated by an example of cadmium concentrations in the topsoil of Northern Ireland through hot spot analysis. In the big data era, further studies should be more inclined to the integration and application of spatial machine learning techniques, as well as investigation on the temporal trends of environmental geochemical features.

Addressing big data variety using an automated approach for data characterization

The creation of new knowledge from manipulating and analysing existing knowledge is one of the primary objectives of any cognitive system. Most of the effort on Big Data research has been focussed upon Volume and Velocity, while Variety, “the ugly duckling” of Big Data, is often neglected and difficult to solve. A principal challenge with Variety is being able to understand and comprehend the data. This paper proposes and evaluates an automated approach for metadata identification and enrichment in describing Big Data. The paper focuses on the use of self-learning systems that will enable automatic compliance of data against regulatory requirements along with the capability of generating valuable and readily usable metadata towards data classification. Two experiments towards data confidentiality and data identification were conducted in evaluating the feasibility of the approach. The focus of the experiments was to confirm that repetitive manual tasks can be automated, thus reducing the focus of a Data Scientist on data identification and thereby providing more focus towards the extraction and analysis of the data itself. The origin of the datasets used were Private/Business and Public/Governmental and exhibited diverse characteristics in relation to the number of files and size of the files. The experimental work confirmed that: (a) the use of algorithmic techniques attributed to the substantial decrease in false positives regarding the identification of confidential information; (b) evidence that the use of a fraction of a data set along with statistical analysis and supervised learning is sufficient in identifying the structure of information within it. With this approach, the issues of understanding the nature of data can be mitigated, enabling a greater focus on meaningful interpretation of the heterogeneous data.

How does cloud computing help businesses to manage big data issues

PurposeBig data has posed problems for businesses, the Information Technology (IT) sector and the science community. The problems posed by big data can be effectively addressed using cloud computing and associated distributed computing technology. Cloud computing and big data are two significant past-year problems that allow high-efficiency and competitive computing tools to be delivered as IT services. The paper aims to examine the role of the cloud as a tool for managing big data in various aspects to help businesses.Design/methodology/approachThis paper delivers solutions in the cloud for storing, compressing, analyzing and processing big data. Hence, articles were divided into four categories: articles on big data storage, articles on big data processing, articles on analyzing and finally, articles on data compression in cloud computing. This article is based on a systematic literature review. Also, it is based on a review of 19 published papers on big data.FindingsFrom the results, it can be inferred that cloud computing technology has features that can be useful for big data management. Challenging issues are raised in each section. For example, in storing big data, privacy and security issues are challenging.Research limitations/implicationsThere were limitations to this systematic review. The first limitation is that only English articles were reviewed. Also, articles that matched the keywords were used. Finally, in this review, authoritative articles were reviewed, and slides and tutorials were avoided.Practical implicationsThe research presents new insight into the business value of cloud computing in interfirm collaborations.Originality/valuePrevious research has often examined other aspects of big data in the cloud. This article takes a new approach to the subject. It allows big data researchers to comprehend the various aspects of big data management in the cloud. In addition, setting an agenda for future research saves time and effort for readers searching for topics within big data.

A Bidirectional Deep-Learning-Based Spectral Attention Mechanism for Hyperspectral Data Classification

Hyperspectral remote sensing presents a unique big data research paradigm through its rich information captured across hundreds of spectral bands, which embodies vital spatial and temporal information about the underlying land cover. Deep-learning-based hyperspectral data analysis methodologies have made significant advancements over the past few years. Despite their success, most deep learning frameworks for hyperspectral data classification tend to suffer in terms of computational and classification efficacy as the data size increases. This is largely due to their equal emphasis criteria on the rich spectral information present in the data, albeit all of the spectral information not being essential for hyperspectral data analysis. On the contrary, this redundant information present in the spectral bands can deter the performance of hyperspectral data analysis techniques. Therefore, in this work, we propose a novel bidirectional spectral attention mechanism, which is computationally efficient and capable of adaptive spectral information diversification through selective emphasis on spectral bands that comprise more information and suppress the ones with lesser information. The concept of 3D-convolutions in tandem with bidirectional long short-term memory (LSTM) is used in the proposed architecture as spectral attention mechanism. A feedforward neural network (FNN)-based supervised classification is then performed to validate the performance of our proposed approach. Experimental results reveal that the proposed hyperspectral data analysis model with spectral attention mechanism outperforms other spatial- and spectral-information-extraction-based hyperspectral data analysis techniques compared.

Hotspot Mining in the Field of Library and Information Science under the Environment of Big Data.

Currently, with the implementation of big data strategies in countries all over the world, big data has achieved vigorous development in various fields. Big data research and application practices have also rapidly attracted the attention of the library and information field. Objective. The study explored the current state of research and research hotspots of big data in the library and information field and further discussed the future research trends. Methods. In the CNKI database, 16 CSSCI source journals in the discipline of library information and digital library were selected as data sources, and the relevant literature was retrieved with the theme of “big data.” The collected literature was excluded and expanded according to the citation relationship. Then, with the help of Bicomb and SPSS, co-word analysis and cluster analysis would be carried out on these literature results. Results. According to the findings of the data analysis, the research hotspots on the topic mainly focus on five major research themes, namely, big data and smart library, big data and intelligence research, data mining and cloud computing, big data and information analysis, and library innovation and services. Limitations. At present, the research scope and coverage on this topic are wide, which leads to the research still staying at the macro level. Conclusions. Big data research will remain one of the hotspots in the future. However, the most study is still limited to the perspective of library and information and has not yet analyzed the research status, research hotspots, and development trends in this field from the perspective of big data knowledge structure. Moreover, machine learning, artificial intelligence, knowledge services, AR, and VR may be new directions for future attention and development.

Development and Trend of Big Data Sharing Research on Health Care during 2014-2020-Based on the Citespace Visualization Analysis

Select the relevant literature on health care big data sharing in CIKI database from 2014 to 2020, use Citespace visual analysis software to show the changing characteristics of health care big data sharing by means of knowledge map from the aspects of research hotspots and trends and academic community, and sort out and analyze this topic. The results show that the topic heat and research trend of health care big data have maintained a stable rise in recent seven years, but no academic community has been formed. It mainly includes the application of health care big data sharing and the problems and countermeasures in the application process. It expounds the application connotation and scope of data sharing, finds that there are problems in information privacy security, legislative guarantee and ethics in the sharing process of health care big data, and puts forward personal suggestions to provide reference for the sharing research of health care big data.

Analysis of College Students' Psychological Education Management in Public Emergencies Based on Big Data.

In recent years, college students' psychological problems have occurred frequently, and the early warning of college students' psychological crisis has received social attention. Artificial intelligence and big data, as emerging technologies that have attracted much attention in recent years, have broad application and development space in improving the development of intelligent and refined education in colleges and universities. Applying artificial intelligence and big data to the practice of college students' mental health education plays a very positive role in accurately finding and scientifically solving college students' mental health problems. This paper combs the current application and research of artificial intelligence and big data in college students' mental health education and then clarifies the problems existing in the practical application. Finally, on the basis of in-depth analysis of the characteristics of college students' psychological crisis, the paper designs college students' psychological crisis early warning data collection system from six aspects, including the educational administration system and the access control system. And from the aspects of establishing a multilevel linkage feedback early warning system, building a team of big data technical personnel and mental health education personnel, it puts forward countermeasures for college students' psychological crisis, so as to provide theoretical and methodological support for college mental health management.

Differential Privacy Technology of Big Data Information Security based on ACA-DMLP

Cloud computing and artificial intelligence have a deeper and closer connection with daily life. To ensure information security, most companies or individuals choose to pay a simple fee to store a large amount of data on cloud servers and hand over a large number of complex calculations of machine learning to cloud servers. To eliminate the security risks of data stored in the cloud and ensure that private data is not leaked, this paper proposes a collusion-resistant distributed machine learning scheme. Through homomorphic encryption algorithm and differential privacy algorithm, the security of data and model in machine learning framework is guaranteed. The distributed machine learning framework is adopted to reduce the data computing time and improve the data training efficiency. The simulation results show that the computational efficiency is improved while the user privacy security is guaranteed. The accuracy of model training is not reduced due to the improvement of privacy data security and computational efficiency. Through this study, we can further propose effective measures for the privacy protection of outsourced data and the data integrity of machine learning, which is of great significance to the security research of cloud intelligent big data.

Ethical issues of collecting, storing, and analyzing geo-referenced tweets for mental health research.

Spatial approaches to epidemiological research with big social media data provide tremendous opportunities to study the relationship between the socio-ecological context where these data are generated and health indicators of interest. Such research poses a number of ethical challenges, particularly in relation to issues such as privacy, informed consent, data security, and storage. While these issues have received considerable attention by researchers in relation to research for physical health purposes in the past 10 years, there have been few efforts to consider the ethical challenges of conducting mental health research, particularly with geo-referenced social media data. The aim of this article is to identify strengths and limitations of current recommendations to address the specific ethical issues of geo-referenced tweets for mental health research. We contribute to the ongoing debate on the ethical implications of big data research and also provide recommendations to researchers and stakeholders alike on how to tackle them, with a specific focus on the use of geo-referenced data for mental health research purposes. With increasing awareness of data privacy and confidentiality issues (even for non-spatial social media data) it becomes crucial to establish professional standards of conduct so that compliance with ethical standards of conducting research with health-related social media data can be prioritized and easily assessed.

Conceptual design of the dual X-ray absorptiometry health informatics prediction system for osteoporosis care.

Osteoporotic fractures are a major and growing public health problem, which is strongly associated with other illnesses and multi-morbidity. Big data analytics has the potential to improve care for osteoporotic fractures and other non-communicable diseases (NCDs), reduces healthcare costs and improves healthcare decision-making for patients with multi-disorders. However, robust and comprehensive utilization of healthcare big data in osteoporosis care practice remains unsatisfactory. In this paper, we present a conceptual design of an intelligent analytics system, namely, the dual X-ray absorptiometry (DXA) health informatics prediction (HIP) system, for healthcare big data research and development. Comprising data source, extraction, transformation, loading, modelling and application, the DXA HIP system was applied in an osteoporosis healthcare context for fracture risk prediction and the investigation of multi-morbidity risk. Data was sourced from four DXA machines located in three healthcare centres in Ireland. The DXA HIP system is novel within the Irish context as it enables the study of fracture-related issues in a larger and more representative Irish population than previous studies. We propose this system is applicable to investigate other NCDs which have the potential to improve the overall quality of patient care and substantially reduce the burden and cost of all NCDs.

Influence Mechanism of Educational Leadership on Environmental Accounting Based on Big Data Algorithm.

Over the last 20 years, big data techniques in teaching have been overgrown. Making educational decisions now includes information knowledge as a crucial component. This started a trend for using big data algorithms strategically. Technological advances have been used to analyze the enormous amount of information and develop strategic judgments. The current study aims to address issues with the conventional instructional, administrative management solution focused on manual rule formulation in big data storage and interpretation and has poor efficiency in analyzing big data and lacking value in developing education leadership qualities. The study suggests an educational leadership model based on big data algorithm (ELM-BDA) to explore the student leadership performance that relies on cooperative filtration and fuzzy C-means (FCM) and big data. The different influencing mechanisms and factors directly linked to educational leadership were also analyzed using a big data algorithm. To build an intelligent institutional administrative system, the research also exposes it to organizational studies. By evaluating the big data research already in existence, this study emphasizes the expanding significance of big data. Additionally, this study explores the effects of big data analytics on educational leadership qualities by utilizing an FCM. A scoring system is designed to predict the student's leadership level, and using the big data algorithms, the students are motivated and trained to improve their skills. The education and learning method can be enhanced at educational institutions through better decision-making to use this big data for leadership development. Big data facilitates efficient educational decision-making by merging various data and telecommunications technologies. Using big data in schooling will increase—leadership quality among students. To effectively use big data for decision-making, academic leaders must create new types of learning and monitoring systems.

Toward Reference Architectures: A Cloud-Agnostic Data Analytics Platform Empowering Autonomous Systems

This work introduces a scalable, cloud-agnostic and fault-tolerant data analytics platform for state-of-the-art autonomous systems that is built from open-source, reusable building blocks. As the baseline for further new reference architectures, it represents an architecture blueprint for processing, enriching and analyzing various feeds of structured and non-structured input data from advanced Internet-of-Things (IoT) based use cases. The platform builds on industry best practices, leverages on solid open-source components in a reusable fashion, and is based on our experience gathered from numerous IoT and Big Data research projects. The platform is currently used in the framework of the National Laboratory for Autonomous Systems in Hungary (abbreviated as ARNL). The platform is demonstrated through selected use cases from ARNL including the areas of smart/autonomous production systems (collaborative robotic assembly) and autonomous vehicles (mobile robots with smart vehicle control). Finally, we validate the platform through the evaluation of its streaming ingestion capabilities.

A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

With the widespread use of mobile and sensing devices, and the popularity of online map-based services, such as navigation services, the volume of spatio-temporal data is growing rapidly. Conventional big data technologies in existing distributed systems cannot effectively process spatio-temporal big data with temporal continuity and spatial proximity. How to construct an effective index for the application requirements of spatio-temporal data in a distributed environment has become one of the hotspots of spatio-temporal big data research. Many spatio-temporal indexing methods have been proposed to support efficient query processing of spatio-temporal data. In this article, the various spatio-temporal big data indexing methods proposed by domestic and foreign researchers from 2010 to 2020 are classified and summarized according to the distributed environment and application background, and the hot issues that need to be paid attention to in the future are proposed according to the changes in application requirements

Some natural hypomethylating agents in food, water and environment are against distribution and risks of COVID-19 pandemic: Results of a big-data research.

Objective:This study analyzes the effects of lifestyle, nutrition, and diets on the status and risks of apparent (symptomatic) COVID-19 infection in Iranian families.Materials and Methods:A relatively extensive questionnaire survey was conducted on more than 20,000 Iranian families (residing in more than 1000 different urban and rural areas in the Islamic Republic of Iran) to collect the big data of COVID-19 and develop a lifestyle dataset. The collected big data included the records of lifestyle effects (e.g. nutrition, water consumption resources, physical exercise, smoking, age, gender, health and disease factors, etc.) on the status of COVID-19 infection in families (i.e. residents of homes). Therefore, an online self-reported questionnaire was used in this retrospective observational study to analyze the effects of lifestyle factors on the COVID-19 risks. The data collection process spanned from May 10, 2020 to March 19, 2021 by selecting 132 samples from more than 40 different social network communities.Results:The research results revealed that food and water sources, which contain some natural hypomethylating agents, mitigated the risks of apparent (symptomatic) COVID-19 infection. Furthermore, the computations on billions of permutations of nutrition conditions and dietary regime items, based on the data collected from people’s diets and infection status, showed that there were many dietary conditions alleviating the risks of apparent (symptomatic) COVID-19 infection by 90%. However, some other diets tripled the infection risk.Conclusion:Some natural hypomethylating agents in food, water, and environmental resources are against the spread and risks of COVID-19.

A Study on Contributor to Sports Development Big Data Research Using Oral Records

The purpose of this study is to analyze the oral records of sports development contributors to explore the direction of big data research on sports development contributors in the future. To this end, the audio file produced in the interview with Lee00, a sports development contributor, was converted into text. The major themes were extracted by analyzing these oral records. The sub-themes were extracted in chronological order. Keywords were extracted by analyzing sub-themes. And the extracted keywords are searched in Google search engine to find related topics and to use them. A Google search for the topic ‘Mt. Inwang’ extracted from the oral archives of Lee00, a contributor to the development of sports, finds newspaper articles about President Moon Jae-in’s climbing Mt. Inwang and opening up Mt. Bukhan. In addition, articles about Mt. Inwang and mountain climbers that the narrator In-jeong Lee speaks are searched for. Through these articles, you can Deriving the theme of the museum exhibition, Collection of museum exhibits, Use as climbing education material.

Evaluating the computational (“Big Data”) turn in studies of media coverage of climate change

AbstractMachine‐assisted big data (MABD) research is enabling quantitative studies of large‐scale social phenomena, including societal responses to climate change. The rise of MABD science is causing both enthusiasm and concerns. Reviewing prominent criticisms of MABD and their relevance for MABD explorations of macro‐structural factors shaping media coverage of climate change, this article finds that the quality and contributions of such studies depend on avoiding common pitfalls. The review focuses specifically on MABD studies' attempts to identify and make sense of correlations—or lack thereof—between climate vulnerability and climate coverage in different countries. The review draws on insights from a single, nationally focused, context‐attentive, and relatively more qualitative “small data” study in the Global South (Brazil) to shed critical light on assumptions, claims, and policy recommendations made based on the computer‐assisted macro‐studies. The review illustrates why more narrowly focused and qualitative small data studies are complementary and indispensable. Besides providing vital understanding of causal relationships that elude MABD studies, more narrowly focused and context‐sensitive qualitative studies can foster understanding of the consequential mediating roles of place‐specific meaning‐making and political strategizing in how climate and weather phenomena are framed by social actors and mass media in particular places. These are dimensions that escape the Big Data quantitative methods, but that are vital to sound policy advice, as illustrated by the Small Data research from Brazil.This article is categorized under: Social Status of Climate Change Knowledge > Knowledge and Practice

The Ethical Implications of Big Data Research in Public Health: "Big Data Ethics by Design" in the UK-REACH Study.

In this article, we analyze legal and ethical issues raised in Big Data health research projects in the Covid-19 era and consider how these issues might be addressed in ways that advance positive values (e.g., furtherance of respect for persons and accordance with relevant legal frameworks) while mitigating or eliminating any negative aspects (e.g., exacerbation of social inequality and injustice). We apply this analysis specifically to UK-REACH (The United Kingdom Research Study into Ethnicity and Covid-19 Outcomes in Healthcare Workers), a project with which we are involved. We argue that Big Data projects like UK-REACH can be conducted in an ethically robust manner and that funders and sponsors ought to encourage similar projects to drive better evidence-based public policy in public health. As part of this, we advocate that a Big Data ethics-by-design approach be undertaken when such projects are constructed. This principle extends the work of those who advocate ethics by design by addressing prominent issues in Big Data health research projects; it holds that ethical values and principles in Big Data health research projects are best adhered to when they are already integrated into the project aims and methods at the design stage. In advocating this principle, we present a unique perspective regarding pressing ethical problems around large-scale, data-driven Covid-19 research, as well as legal issues associated with processing ostensibly anonymized health data.

Constructing a multiple‐part morphospace using a multiblock method

Abstract Popular current methods for quantifying variation in biological shape are well‐suited to analyses of isolated parts (e.g. the same bone from the skeletons of many individuals). An analytical challenge exists for quantifying variation between the shapes of multiple‐part objects where each part has a different position, rotation or scale (e.g. partial or whole articulated skeletons). We investigated regularised consensus principal component analysis (RCPCA) as a multiblock method for quantifying variation in the shape of multiple‐part objects. Multiblock methods are routinely used in other big data research fields such as bioinformatics/medicine, marketing and food research, but have not been widely embraced for evolutionary biology research. We have created the new package morphoBlocks for the r programming language to make RCPCA more accessible for shape evolution research. morphoBlocks provides a complete workflow for formatting, analysing and visualising the variation between multiple‐part objects by integrating functions from a diverse range of other packages. In particular, global components produced by RCPCA provide a consensus space that we present here as a morphospace for multiple‐part objects. morphoBlocks is demonstrated with a case study of manually placed landmarks and automatically placed pseudolandmarks from the partial wing skeletons of 15 extant penguin species and five fossil penguin species. Our case study provides quantitative support for a historical hypothesis about the magnitude and mode of morphological change across the evolutionary history of penguins. RCPCA can be used to analyse two‐ or three‐dimensional datasets with 10s of landmarks, or 100s to 1,000s of semilandmarks or pseudolandmarks, from 10s to 100s of specimens comprised of two or more parts. We use morphoBlocks on a small three‐bone case study and provide a framework for applying this method to much larger studies investigating the ecological or evolutionary significance of multiple‐part objects.

Binary Classification: An Introductory Machine Learning Tutorial for Social Scientists

A barrier that prevents many social scientists from pursuing big data research is the lack of technical training required to assemble and organize big data. In an effort to address this barrier, we provide an introductory tutorial into machine learning for social scientists by demonstrating the basic steps and fundamental concepts involved in binary classification. We first describe the data and libraries required for analysis. We then demonstrate data cleaning methods, feature engineering, the model-building process, model assessment, and feature importance. Last, we discuss the ways in which social scientists can use machine learning to complement inference-based approaches and how it can contribute to a richer understanding of social science.