Big Research Research Articles

Big Research Data Management Needs of Researchers at a Research- Intensive University - A Qualitative Study

Big Research Data Management (BRDM) infrastructure is a complex socio-technical system. The design and provision of effective BRDM services at research universities require a multipronged examination of individual, social, technical, and larger contextual (i.e., campus, state, and national context) aspects of the BRDM infrastructure needs and uses of their users. Although there is a significant body of literature on BRD already, an in-depth, theory-driven examination of researchers’ needs for BRDM services on university campuses and the challenges and hurdles they face in using and managing BRD is lacking. Furthermore, there is a dearth of research on university administrators’ motivation for providing BRD services on their campuses and the hurdles and barriers they face in providing those services.This study aims to contribute to the literature by investigating the BRD-related needs of researchers from different fields and the barriers and challenges to designing and providing such support at a research university. The ongoing Health Data Science Initiative (HDSI) of Florida State University and user interviews are used as the data sources of this study. The study is guided by activity theory to investigate the complex structure of activities and factors that make BRDM infrastructure successful on university campuses.When completed, the study will inform the design of BRDM infrastructure and services on university campuses by providing an evidence base for the perspectives and needs of BRD creators, users, curators, and policymakers. In particular, by understanding the BRDM requirements of researchers, university administrators can provide effective and user-centered BRDM solutions to them.

Expert consensus on the application of big data on rehabilitation medicine (2024 edition)

With the development and progress of technology, the transformation of biomedical models, and the application of digital technology, the field of rehabilitation medicine has also generated massive and diversified big data. However, the current level of utilization of rehabilitation medicine is relatively low. Empowering the development of rehabilitation medicine through big data is crucial. In order to better promote the standardized development of rehabilitation big data in China, the Rehabilitation Big Data Working Committee of the Chinese Rehabilitation Medical Association organized an expert plan on the concept, significance, and necessity of rehabilitation big data; Rehabilitation big data source; Priority areas for rehabilitation big data application research; Basic research methods for rehabilitation big data; The standards and principles for establishing a rehabilitation database; Consensus has been developed on six aspects of clinical application scenarios for rehabilitation big data, providing reference for healthcare workers and researchers related to rehabilitation big data.

Data protection legislation in Africa and pathways for enhancing compliance in big data health research

BackgroundThe increasing availability of large volumes of personal data from diverse sources such as electronic health records, research programmes, commercial genetic testing, national health surveys and wearable devices presents significant opportunities for advancing public health, disease surveillance, personalized medicine and scientific research and innovation. However, this potential is hampered by a lack of clarity related to the processing and sharing of personal health data, particularly across varying national regulatory frameworks. This often leaves researcher stakeholders uncertain about how to navigate issues around secondary data use, repurposing data for different research objectives and cross-border data sharing.MethodWe analysed 37 data protection legislation across Africa to identify key principles and requirements for processing and sharing of personal health and genetic data in scientific research. On the basis of this analysis, we propose strategies that data science research initiatives in Africa can implement to ensure compliance with data protection laws while effectively reusing and sharing personal data for health research and scientific innovation.ResultsIn many African countries, health and genetic data are categorized as sensitive and subject to stricter protection. Key principles guiding the processing of personal data include confidentiality, non-discrimination, transparency, storage limitation, legitimacy, purpose specification, integrity, fairness, non-excessiveness, accountability and data minimality. The rights of data subjects include the right to be informed, the right of access, the right to rectification, the right to erasure/deletion of data, the right to restrict processing, the right to data portability and the right to seek compensation. Consent and adequacy assessments were the most common legal grounds for cross-border data transfers. However, considerable variation exists in legal requirements for data transfer across countries, potentially creating barriers to collaborative health research across Africa.ConclusionsWe propose several strategies that data science research initiatives can adopt to align with data protection laws. These include developing a standardized module for safe data flows, using trusted data environments to minimize cross-border transfers, implementing dynamic consent mechanisms to comply with consent specificity and data subject rights and establishing codes of conduct to govern the secondary use of personal data for health research and innovation.

A comparative study on the perception of integrated resort in Korea before and after COVID-19 using social big data

The study was conducted to compare and analyze perceptions of integrated resorts before and after the outbreak of COVID-19 and to explore ways to revitalize the operation of integrated resorts that can be utilized in the post-Corona era. Keywords highly related to integrated resorts were collected from major social media and SNS channels in Korea, such as Naver, Daum, and Google. Unstructured big data analysis research was conducted using social network analysis and semantic network analysis, which are major methodologies for social big data. The keyword data collection period was divided into the time before and after the outbreak of COVID-19. A total of 7,933 keywords were collected before the outbreak, and 7,395 were collected during the period after the outbreak. The study included a total of 100 keywords, with 50 from each period. The research results showed that, first, before the outbreak of COVID-19, the keywords ‘Casino’, ‘Yeongjong-do’, ‘Hotel’, ‘Dream Tower’, ‘Incheon’, ‘Development’, ‘Domestic’, and ‘Business’ appeared with high frequency. After the outbreak, the keywords ‘Jeju’, ‘Resort’, ‘Dream Tower’, ‘Hotel’, ‘Inspire’, and ‘Business’ showed high frequencies of appearance. Second, CONCOR analysis confirmed the presence of seven keyword clusters before and after the outbreak of COVID-19. Based on the results of this study, various implications for integrated resorts that can be utilized in the post-Corona era suggested, along with future directions for integrated resort research.

Data Lakehouse: A survey and experimental study

Efficient big data management is a dire necessity to manage the exponential growth in data generated by digital information systems to produce usable knowledge. Structured databases, data lakes, and warehouses have each provided a solution with varying degrees of success. However, a new and superior solution, the data Lakehouse, has emerged to extract actionable insights from unstructured data ingested from distributed sources. By combining the strengths of data warehouses and data lakes, the data Lakehouse can process and merge data quickly while ingesting and storing high-speed unstructured data with post-storage transformation and analytics capabilities. The Lakehouse architecture offers the necessary features for optimal functionality and has gained significant attention in the big data management research community. In this paper, we compare data lake, warehouse, and lakehouse systems, highlight their strengths and shortcomings, identify the desired features to handle the evolving challenges in big data management and analysis and propose an advanced data Lakehouse architecture. We also demonstrate the performance of three state-of-the-art data management systems namely HDFS data lake, Hive data warehouse, and Delta lakehouse in managing data for analytical query responses through an experimental study.

When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data.

Despite the lack of consensus on an official definition of Big Data, research and studies have continued to progress based on this "no consensus" stance over the years. However, the lack of a clear definition and scope for Big Data results in scientific research and communication lacking a common ground. Even with the popular "V" characteristics, Big Data remains elusive. The term is broad and is used differently in research, often referring to entirely different concepts, which is rarely stated explicitly in papers. While many studies and reviews attempt to draw a comprehensive understanding of Big Data, there has been little systematic research on the position and practical implications of the term Big Data in research environments. To address this gap, this paper presents a Systematic Literature Review (SLR) on secondary studies to provide a comprehensive overview of how Big Data is used and understood across different scientific domains. Our objective was to monitor the application of the Big Data concept in science, identify which technologies are prevalent in which fields, and investigate the discrepancies between the theoretical understanding and practical usage of the term. Our study found that various Big Data technologies are being used in different scientific fields, including machine learning algorithms, distributed computing frameworks, and other tools. These manifestations of Big Data can be classified into four major categories: abstract concepts, large datasets, machine learning techniques, and the Big Data ecosystem. This study revealed that despite the general agreement on the "V" characteristics, researchers in different scientific fields have varied implicit understandings of Big Data. These implicit understandings significantly influence the content and discussions of studies involving Big Data, although they are often not explicitly stated. We call for a clearer articulation of the meaning of Big Data in research to facilitate smoother scientific communication.

Floristic status and analyses of the mangrove marine park in Muanda (central Kongo) and the Kimvula-Mitendi experimental site in Kinshasa (democratic republic of Congo)

Human activities and climate change affect mangrove biotopes. This study is part of the search for a space favorable to the domestication of these species in the ecological landscape of Kinshasa (DRC) for their sustainable management. Plant species were collected, identified, and analyzed for biological, ecological and phytogeographical parameters determination. A total of 38 species were identified, among which 14 were from Km5-Banana (Muanda) and 24 from Kimvula-Mitendi (Kinshasa) sites. Magnoliophyta represent 92.86% of species versus 7.14% of Polypodiophyta. The generic coefficient of Jaccard was estimated to 92.86% at Km5-Banana and 100% at Kimvula-Mitendi. Sorensen’s evidence of similarities between the two sites reveals that the two sites were very different. The forest at Kimvula-Mitendi is heavily exploited and the mangrove forest at Km5-Banana seriously degraded. Domestication success of mangrove plant species from Km5-Banan in the biotope of Kinshasa may result in a big challenge and need suitable research and technical strategies.

Comprehensive single cell transcriptomics analysis of murine osteosarcoma uncovers Skp2 function in metastasis, genomic instability and immune activation and reveals additional target pathways.

Osteosarcoma (OS) is the most common primary pediatric bone malignancy. One promising new therapeutic target is SKP2, encoding a substrate recognition factor of the SCF E3 ubiquitin ligase responsible for ubiquitination and proteasome degradation of substrate p27, thus driving cellular proliferation. We have shown previously that knockout of Skp2 in an immunocompetent transgenic mouse model of OS improved survival, drove apoptosis, and induced tumor inflammation. Here, we applied single-cell RNA-sequencing (scRNA-seq) to study primary OS tumors derived from Osx-Cre driven conditional knockout of Rb1 and Trp53. We showed that murine OS models recapitulate the tumor heterogeneity and microenvironment complexity observed in patient tumors. We further compared this model with OS models with functional disruption of Skp2: one with Skp2 knockout and the other with the Skp2-p27 interaction disrupted (resulting in p27 overexpression). We found reduction of T cell exhaustion and upregulation of interferon activation, along with evidence of replicative and endoplasmic reticulum-related stress in the Skp2 disruption models, and showed that interferon induction was correlated with improved survival in OS patients. Additionally, our scRNA-seq analysis uncovered decreased activities of metastasis-related gene signatures in the Skp2-disrupted OS, which we validated by observation of a strong reduction in lung metastasis in the Skp2 knockout mice. Finally, we report several potential mechanisms of escape from targeting Skp2 in OS, including upregulation of Myc targets, DNA copy number amplification and overexpression of alternative E3 ligase genes, and potential alternative lineage activation. These mechanistic insights into OS tumor biology and Skp2 function suggest novel targets for new, synergistic therapies, while the data and our comprehensive analysis may serve as a public resource for further big data-driven OS research.

Thermal – chemical - mechanical characterization of Anacardium occidentale tree gum

Anacardium occidentale (cashew) tree gum is being used in several sectors, including the pharmaceutical sector. This gum has been explored more in the medical field by many previous researchers, but there is a big research gap regarding its thermal and mechanical properties. Therefore, this research is intended to reveal the thermal, chemical, and mechanical characteristics of Anacardium occidentale tree gum. The results obtained in this regard are then compared with certain properties of artificial resins. Thermal analysis is carried out using a thermogravimetric analyzer, and differential scanning calorimeter, elemental analysis is carried out using a scanning electron microscope and a micro-X-ray fluorescence analyzer; and mechanical tests are carried out using a nano-indentation tester and a universal testing machine. The pH of 4.76 shows that the gum is acidic in nature, and the peaks obtained from thermal analysis demonstrate that it doesn't have a melting point. The microhardness value, tensile strength, flexural strength, and compressive strength of cashew gum are 218.39 MPa, 1.667 MPa, 3.64 MPa, and 2.667 MPa, respectively. The obtained results show that, Anacardium occidentale tree gum has comparable thermal properties to those of artificial resins and other natural gums.

A Systematic Review of Big Data Driven Education Evaluation

The rapid development of artificial intelligence has driven the transformation of educational evaluation into big data-driven. This study used a systematic literature review method to analyzed 44 empirical research articles on the evaluation of big data education. Firstly, it has shown an increasing trend year by year, and is mainly published in thematic journals such as educational technology, science education, and language teaching. Chinese and American researchers have made the greatest contributions in this field. Secondly, the algorithmic models for big data education evaluation research are diverse, the text modality is the most popular, the evaluation subjects are mainly college students, with fewer primary and secondary school students, and science is the discipline that most commonly applies big data education evaluation. The evaluation objectives of big data education evaluation mainly focus on five aspects: high-order thinking analysis, learning performance prediction, learning emotion recognition, teaching management decision-making, and evaluation mode optimization, and the text modality is widely used for data collection in high-order thinking analysis; regardless of the evaluation objectives, higher education students are the most widely evaluated objects; the science discipline is the main field of using big data technology to empower teaching evaluation. Thirdly, the current research topics of big data education evaluation mainly focus on online learning behavior and environmental participation evaluation, process assessment of learning motivation and emotional analysis, development and optimization of subject domain big data models, cognitive diagnosis and high-order thinking skills evaluation, and design of learning analysis frameworks based on data mining.

The current state of big data analytics research in marketing: A systematic review using TCCM approach

ABSTRACT There has been an explosive interest in big data analytics among marketing academics. Despite the growing scholarly interest, there has been a lack of effort in reviewing the past studies on this topic. Based on bibliometric analyses and drawing on the Theory-Context-Characteristics-Methods (TCCM) framework, the current study provides a comprehensive understanding of the common theories, contexts (i.e. industries and countries), characteristics (i.e. key variables and their relationships), and methods (i.e. research methods and analytical techniques) employed in this research domain. Through a systematic review of 64 articles, four distinct themes were identified. Analyses also revealed a general divide in findings between studies that used different research methodologies (i.e. big data vs. traditional method). The current research concludes with implications and directions for future research.

An efficient estimator of population variance of a sensitive variable with a new randomized response technique

In sampling theory, a majority of the available estimators of population variance are designed for use with non-sensitive variables only. Such estimators cannot perform efficiently when the variable of interest is of sensitive nature, such as use of drugs, illegal income, abortion, cheating in examination, the amount of income tax payable, and the violation of rules by employees, etc. In the current literature, the shortage of research studies on variance estimators of a sensitive variable has created a big research gap and a room for improvement in the efficiency of such estimators. In this paper, a new randomized scrambling technique is proposed, along with a new estimator of population variance. The new estimator achieves improvement in efficiency over the available variance estimators. The proposed estimator is designed for use with simple random sampling and uses the information on an auxiliary variable. The improvement in efficiency is shown for different choices of constants. Besides efficiency, improvement in the unified measure of estimator quality is also achieved with the proposed estimator under the new randomized response model.

A Real Time Deep Learning Based Approach for Detecting Network Attacks

Anomaly-based Intrusion Detection is a key research topic in network security due to its ability to face unknown attacks and new security threats. For this reason, many works on the topic have been proposed in the last decade. Nonetheless, an ultimate solution, able to provide a high detection rate with an acceptable false alarm rate, has still to be identified. In the last years big research efforts have focused on the application of Deep Learning techniques to the field, but no work has been able, so far, to propose a system achieving good detection performance, while processing raw network traffic in real time. For this reason in the paper we propose an Intrusion Detection System that, leveraging on probabilistic data structures and Deep Learning techniques, is able to process in real time the traffic collected in a backbone network, offering excellent detection performance and low false alarm rate. Indeed, the extensive experimental tests, run to validate our system and compare different Deep Learning techniques, confirm that, with a proper parameter setting, we can achieve about 92% of detection rate, with an accuracy of 0.899. Finally, with minimal changes, the proposed system can provide some information about the kind of anomaly, although in the multi-class scenario the detection rate is slightly lower (around 86%).

Big data-driven water research towards metaverse

Although big data is publicly available on water quality parameters, virtual simulation has not yet been adequately adapted in environmental chemistry research. Digital twin is different from conventional geospatial modeling approaches and is particularly useful when systematic laboratory/field experiment is not realistic (e.g., climate impact and water-related environmental catastrophe) or difficult to design and monitor in a real time (e.g., pollutant and nutrient cycles in estuaries, soils, and sediments). Data-driven water research could realize early warning and disaster readiness simulations for diverse environmental scenarios, including drinking water contamination.

Pharmaceuticals and Personal Care Products in the Environment: What Progress Has Been Made in Addressing the Big Research Questions?

Pharmaceuticals and Personal Care Products in the Environment: What Progress Has Been Made in Addressing the Big Research Questions?

국어 명사절, 관형절의 논리-의미 유형 연구

Objectives In the clause complexing systems of systemic functional linguistics, the logico-semantic types(LSTs) between ‘clause’ and ‘clause’ are realised in different ways in coordinate clauses, subordinate clauses, and em-bedded clauses. The purpose of this paper is to identify the LSTs of Korean nominal clauses and adjective clauses that are distinct form other embedded clauses.
 Methods SFL's clause complexing system consists of three optinal systems: taxis, LSTs, and repercussions. Among the LSTs of Korean embedded clauses, three are used: [projection], [phenomenon], and [extension]. These are realised in different ways in quotation clauses, nominal clauses, and adjevtive clauses, repectively.
 Results After applying the aforementioned methodology, we were able to establish [phenomenon] of the com-plement participants of mental verbs as the common LST of Korean nominal clauses. Based on this, it was further found that the nominal clause ‘-기’ combined with desrative verbs and emotional verbs has the LST of [phenom-enon_ideas]; and the nominal clause ‘-음’ combined with cognitive verbs has the LST of [phenomenon_facts]; and the nomonal clause ‘-는 것’ combined with perceptual verbs has the LST of [phenomenon_acts]. On the other hand, the LSTs of adjevtive clauses were broadly distinguished as [projection] and [expansion]. The [projection] semantics of adjevtive clauses are mainly realised in the complement clauses of superordinate Projective Nouns and Factual Nouns.
 Conclusions The LSTs between ‘clause’ and ‘clause’ in clause-complexing systems in systemic functional lin-guistics are big research topics. This can be realised in different ways in coordinate, subordinate and embedded clauses. So far, this paper focuses on the LSTs that appear in nominal clauses and adjective clauses, examining them in detail using examples from the Korean language dictionary, and summarising the results. It is hoped that further research could conduct to investigate the LSTs of the entire Korean clause complexing systems.

Research and publication ethics: pillars of integrity in the scientific medical journal

Research has been the foundation upon which human development has always been built. For sustainable growth, this foundation needs to be based on trustworthy knowledge, which cannot be achieved without following ethical standards at the highest possible level. Research has been conducted in various forms at different levels across all fraternities. There is no small or big research, there is only reliable or unreliable research. Research at different levels yields different levels of evidence which often builds up enough material to define a course of action, leading to the formation of guidelines or protocols.

키워드 네트워크 분석을 통해 살펴본 저출산 관련 보육정책에 대한 역대 정부별 연구동향 분석

Objectives The purpose of this study is to provide directions for child-care support policies related to low fertility by examining the research trends of successive government on child-care policies related to low fertility using keyword network analysis.
 Methods For this purpose, thesis and KCI academic journal D/B provided by the Research Information Sharing Service of the Korea Education and Research Information Service were used to collect rawDATA with keywords such as ‘low fertility’ and ‘few births’. A 1-mode matrix dataset was created based on the data that had been systematically reviewed and the first and second refinements were completed, and a keyword network analysis was performed on the entire and successive government.
 Results The results of this study were as follows: First, by thesis type, academic journals had the most, and by policy area, the power was the most. Second, as a result of examining research trends by successive governments, the first period was named ‘the introductory period of state-led fertility promotion policy research to improve the fertility rate’, the second period was named ‘the transition period of fertility promotion policy research to improve the quality of life’, and the third period was named ‘the strengthening period of big data-based fertility promotion policy research to explore social awareness’, which indicates that the flow of research changes from the 1st to the 3rd stage.
 Conclusions Based on these analysis results, it is possible to confirm the overall trend of child-care policies related to low fertility and research trends by successive governments, as well as suggest implications for the preparation of childcare support policies related to low fertility.

A Descriptive Study on- Scope of Space Research for Start-up With Reference to Indian Space Research Opportunities

The space research scene in India is on the rise, and this study takes a closer look at what's happening. We explore how big the space research market is, what's making it grow, and who the key players are. In India, space research is booming thanks to cool projects and better technology. We check out the main players, from new start-ups to the big research centres, and see how they're making their mark in this busy space. The city is home to lots of space research spots, each doing different things like making satellites and exploring outer space. We look at the city's tech strengths and skilled pros, predicting that India is all set for more growth and cool stuff in space research. This study gives you a peek into what's happening now and what's in store for India as it becomes a major player in India's space exploration dreams.

Application of Big Data Technology Combined with Clustering Algorithm in Manufacturing Production Analysis System

Production data analysis is crucial for production planning in the manufacturing industry, and accurate and a comprehensive and accurate analysis can improve production planning. To analyze the production of manufacturing industry, the research proposes the big data technology research method of the set clustering algorithm. In the process of this research method, the K-means clustering algorithm is first used to build the production measurement data system, and the Apache Hadoop Big data framework is applied. Then it introduces the Apriori algorithm for data association mining, and finally uses the genetic algorithm for production scheduling in view of big data analysis. The experiment showcases that the research method achieved a support level of 0.002 with 729 association rules when testing the number of association rules. When conducting data throughput testing, the research method achieved a data throughput of 7789 threads/s when the number of threads reached 8 in the Sleep scenario. In the analysis error testing, the error rate of the research method in the retained data fluctuates around 3.1%. When testing the number of processing operations in the process, the maximum error in the analysis results of the processing operations in the research method is 2. The results indicate that the research method possesses exceptional computational performance, carrying out manufacturing production analysis effectively and efficiently. The manufacturing production analysis system designed by the research institute offers valuable reference solutions for the informationization and intelligent development of the manufacturing industry.