Big Data Science Research Articles

The History Of Genius Discovery Software

This article description the conception of History of Genius Discovery (History GD) software. The software has few similarities with GitHub software that have got wide famous at the professional developer’s community. The software appealed to solve two main science issues. History GD will save science and cultural heritage of Russian scientists and accumulate initial data for measuring tendencies of science theorems formation. The last will give the probability for appending The Structure of Scientific Revolutions by Thomas S. Kuhn by using numeric big data. Also, the software will minimise probability of losing scientific manuscripts by reason of scientists deaths. Software engineering, sociology, philosophy, law, and history are five scientific directions that are used as base for creating this software. The idea of creation have got at Kazan Federal University when we learned Big Data Science.

Fine modeling of nasal cavity and its application in precision surgical design

In recent years, the research hotspot of rhinology is looking forward to effective method for changing the anatomical structure of nasal cavity and paranasal sinus. And it will build up the optimal relationship between airflow field and biological effects in rhinology surgency. Now in precise medical treatment, we are eager to find a more reasonable and optimized surgical method before operation, with the help of information technology and big data science. It will not only achieve the greatest biological effect, but also maximize the protection of the lateral wall of the nasal cavity, in order to eliminate the potential recurrence of sinusitis. This paper mainly includes three parts: status and trends of nasal cavity modeling, biomechanics research progress and progress in evaluation of nasal surgery. It is very helpful for promoting the applying of up-to-date model in precise surgical design.

An Applied Healthcare Data Science Roadmap for Nursing Leaders: A Workshop Development, Conceptualization, and Application.

Nurse leaders working with large volumes of interdisciplinary healthcare data are in need of advanced guidance for conducting analytics to improve population outcomes. This article reports the development of a roadmap to help nursing leaders use data science principles and tools to inform decision-making, thus supporting research and approaches in clinical practice that improve healthcare for all. A consensus-building and iterative process was utilized based on the Cross-Industry Standard Process for Data Mining approach to big data science. Using the model, a set of components are described that combine and achieve a process for data science projects applicable to healthcare issues with the potential for improving population health outcomes. The roadmap was tested using a workshop format. The workshop was presented to two audiences: nurse leaders and informatics/healthcare leaders. Results were positive and included suggestions for how to further refine and communicate the roadmap.

The Byzantine Role of Big Data Application in Nursing Science: A Systematic Review.

Big data have the potential to determine enhanced decision-making process and to personalize the approach of delivering care when applied in nursing science. So far, the literature on this topic is still not synthesized for the period between 2014 and 2018. Thus, this systematic review aimed to identify and synthesize the most recent evidence on big data application in nursing research. The systematic search was undertaken for the evidence published from January 2014 to May 2018, and the outputs were formatted using the PRISMA Flow Diagram, whereas the quality appraisal was addressed by recommendations consistent with the Critical Appraisal Skills Program. Twelve studies on big data in nursing were included and divided into two themes: the majority of the studies aimed to determine prediction assessment, while only four studies were related to the impact of big data applications to support clinical practice. This review tracks the recent state of knowledge on big data applications in nursing science, revealing the potential for nursing engagement in big data science, even if currently limited to some fields. Big data applications in nursing might have a tremendous potential impact, but are currently underused in research and clinical practice.

A New Distributed Type-2 Fuzzy Logic Method for Efficient Data Science Models of Medical Informatics

The paper aims to propose a distributed method for machine learning models and its application for medical data analysis. The great challenge in the medicine field is to provide a scalable image processing model, which integrates the computing processing requirements and computing-aided medical decision making. The proposed Fuzzy logic method is based on a distributed approach of type-2 Fuzzy logic algorithm and merges the HPC (High Performance Computing) and cognitive aspect on one model. Accordingly, the method is assigned to be implemented on big data analysis and data science prediction models for healthcare applications. The paper focuses on the proposed distributed Type-2 Fuzzy Logic (DT2FL) method and its application for MRI data analysis under a massively parallel and distributed virtual mobile agent architecture. Indeed, the paper presents some experimental results which highlight the accuracy and efficiency of the proposed method.

National center for interprofessional practice and education IPE core data set and information exchange for knowledge generation

ABSTRACT Since 2012, the National Center for Interprofessional Practice and Education has worked with over 70 sites implementing over 100 interprofessional education and collaborative practice (IPECP) programs in the United States (U.S.). Program leaders have contributed data and information to the National Center to inform an approach to advancing the science of interprofessional practice and education (IPE), called IPE Knowledge Generation. This paper describes how the evolution of IPE Knowledge Generation blends traditional research and evaluation approaches with the burgeoning field of health informatics and big data science. The goal of IPE Knowledge Generation is to promote collaboration and knowledge discovery among IPE program leaders who collect comparable, sharable data in an information exchange. This data collection then supports analysis and knowledge generation. To enable the approach, the National Center uses a structured process for guiding IPE program design and implementation in practice settings focused on learning and the Quadruple Aim outcomes while collecting the IPE core data set and the contribution of contemporary big data science.

Application of Geometric Modeling in Visualizing the Medical Image Dataset

Geometric modeling is a research based on computational geometry for processing the 3D objects in computer graphics or image processing. Rendering the graphical objects in medical applications is an interesting research that can help doctors have an exact decision in their diagnostic method. Application of geometric modeling in visualization of medical data is considered as a tool to support treatment. In the field of big data analysis and data science, the methods, tools and techniques are essential and important to analyze a massive amount of information and make data-driven decisions. These data are first analyzed and computed depending on the criteria and purpose of users. They are then visualized or simulated using visual elements like charts, graphs or maps to explore, understand their characteristics and data structure. Which method or solution is best for rendering and visualizing the analyzed data? That is still a question and even a challenge to the researchers. In this article, we apply geometric modeling to build a web-based application for visualizing a medical image dataset. This is an improved version of the selected paper in the previous work [1]. We first review several researches in the fields of computer graphics, images processing, geometric modeling and their applications; then we combine all of them and propose a method to develop our application for reconstructing the medical data objects. The input data are a Dicom (Digital Imaging and Communications in Medicine) dataset captured from CT (computed tomography) scanner or MRI (Magnetic Resonance Imaging). The output are 2D and 3D models that can be displayed and handled in different ways on the web interface. In comparison with other existing applications, our method shown the advantages of its using in practice.

Social capital across the life course: Accumulation, diminution, or segregation?

AbstractThis study examines changes in individual social capital during adult life within a 19-year period. Social capital theory and life course theory are combined, and it is argued that changes in social networks do not necessarily go together with changes in social capital: while personal networks are known to decline in size with age, social capital can be expected to accumulate, in particular for those who had a better starting position and therefore more resources to share. Panel data from the survey of the social networks of the Dutch (SSND) (1999–2018) at four points of measurement are employed to inquire into this argument. Social capital is measured by the position generator instrument, and three indicators, that is, resource extensity, mean prestige access, and resource range are analyzed. Results of fixed effect models show that, on average, people maintain access to social capital, and that men and higher educated gain social capital through their life as opposed to women and lower educated. Implications for the understanding of the reproduction of social inequality are discussed. The paper concludes with a reflection upon the value of ego-centered network analysis in the era of big data and data science.

Development of an Innovative Tool to Appraise Big Data for Best Evidence.

Datum from electronic sources has accumulated and resulted in the establishment of big data and data science. Big data consists of data sets that are larger than traditional data processing applications can manage. Data science is the research method used to analyze big data. Researchers are applying research methods to harness large and complex data sets to increase our understanding of population health by creating predictive models of patients using a variety of key variables or characteristics. Evidence-based practice relies on the appraisal of research to ensure rigor prior to implementation in clinical settings. Consistent with other research methods, papers based on data science should be subject to appraisal for determination of best evidence. The purpose of this paper is to present a tool that can be used to appraise research papers based on large data sets and data science research methods. The following approach was used to develop the Data Science Appraisal Tool (DSAT). Despite an exhaustive search, we were unable to locate an appraisal tool for papers based on data science research methods. We then synthesized the extant literature to form the tool. The tool is based on the common characteristics of big data: (a) verification that the data set is representative of big data; (b) preparation of the data for analysis; (c) methodology used for data analysis; (d) results; and (e) theoretically based. Appraisal tools currently exist for traditional and well-known research methods. The DSAT provides a method to appraise papers based in data science for best evidence.

Foregrounding data in co-design – An exploration of how data may become an object of design

Abstract The rise of Big Data and data science has prompted a focus on data as an essential component of making and innovating data-based services. Traditionally, however, digital data has not been object to co-design as have other physical or functional dimensions of IT application design. This is problematic, because it hinders domain experts who are not IT professionals from taking part in the discussions and design of the data-based services they use and provide. We argue that to address this challenge, it is necessary to empower such domain experts to be able to consider data as an object of design, so they may contribute their expertise to the design of data-based services and their underlying data structures. This paper describes how data may be foregrounded as an explicit element of design that support domain experts’ understanding of data as something that can be designed. We present a detailed interaction analysis of video recordings of three collaborative design workshops, in which we propose a form of data notation and two data representations. We find that data may become an object of design for domain experts when tangible and flexible representations of data are used. Based on our findings, we discuss five lessons learned for foregrounding data in co-design. Together, these provide practical insights for future work.

Recent trends & applications of big data science in chronobiology

With the advent of sequencing technologies, now we have observed rapid advancements in biological research. Avenue of big data science steered the development of many new areas of interests with life science researchers. Approaches using big data mining and machine learning (ML) along with other well-established methods of genomics, molecular biology and biotechnology may be implemented with researches in chronobiology. Methodologies demonstrating usage of these techniques with “omics” studies have shown a noteworthy impact on the results of the same. On the other hand, data science unaided will not reject the need for a considerate understanding of the field of research, nor can such approaches substitute the need for researchers and analysts. Hence, so far understanding with this region of investigation is significantly important. It is obvious that the familiarity which has been generated via chronobiology and the applications of various tools resulting from big data science has backed up the acknowledgement of the various patterns and immense biological rhythms reported in organisms. The present text goals to create novel and vital applications in the coming future through chronobiology research orchestrating with big data science.

Fundamentals of deep data science


 
 
 The present study reports the fundamentals of deep data sciences and their emerging roles across the globe. The influence of deep data science plays important roles in information science. The tools and softwares designed using big data science are creating huge impact on the society. Keeping these into consideration, the study reports the beneficial aspect of it along with basic information.
 
 

Rosacea Patients Are at Higher Risk for Obstructive Sleep Apnea: Automated Retrospective Research.

Using big data science we employ NLP and a novel interface the BMI Investigator to answer clinically meaninful questions. The use case presented is the association between Rosacea and Obstructive Sleep Apnea.

Machine Learning Pipelines with Modern Big Data Tools for High Energy Physics

The effective utilization at scale of complex machine learning (ML) techniques for HEP use cases poses several technological challenges, most importantly on the actual implementation of dedicated end-to-end data pipelines. A solution to these challenges is presented, which allows training neural network classifiers using solutions from the Big Data and data science ecosystems, integrated with tools, software, and platforms common in the HEP environment. In particular, Apache Spark is exploited for data preparation and feature engineering, running the corresponding (Python) code interactively on Jupyter notebooks. Key integrations and libraries that make Spark capable of ingesting data stored using ROOT format and accessed via the XRootD protocol, are described and discussed. Training of the neural network models, defined using the Keras API, is performed in a distributed fashion on Spark clusters by using BigDL with Analytics Zoo and also by using TensorFlow, notably for distributed training on CPU and GPU resourcess. The implementation and the results of the distributed training are described in detail in this work.

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.

Application of machine learning methods for predicting hazardous failures of railway track assets

The Aim of the paper is to reduce the number of hazardous events on railway tracks by developing a method of prediction of rare hazardous failures based on processing of large amounts of data on each kilometre of track obtained in real time from diagnostics systems. Hazardous failures are rare events; the set of variate values of the number of such events for an individual kilometre of track per year is: [0, 1]. However, for a railway network as a whole the yearly number of such events is in the dozens and efficient management requires the transition from the estimation of the probability of hazardous failure occurrence to the identification of the most probable location of failure. Methods . The problem of identification of rare, but hazardous possible events out of hundreds of thousands of records of non-critical railway track parameter divergences cannot be solved by conventional means of statistical processing. Hazardous events are predicted using the above statistics and artificial intelligence. Big Data and Data Science technology is used. Such technology includes methods of machine learning that enable item classification based on characteristics (features, predicates) and known cases of undesired event occurrence. The application of various algorithms of machine learning is demonstrated using the example of prediction of track superstructure failures using records collected between 2014 and 2019 on the Kuybyshevskaya Railway. Findings and conclusions . The result of facility ranking is the conclusion regarding the location of the most probable hazardous failure of railway track. That conclusion is based on the correspondence analysis between the actual characteristics of an item and conditions of its operation and the cases of adverse events and cases of their non-occurrence. The practical value of this paper consists in the fact that the proposed set of methods and means can be considered as an integral part of the track maintenance decision-making system. It can be easily adapted for online operation and integrated into the automated measurement system installed on a vehicle.

CloudFinder: A System for Processing Big Data Workloads on Volunteered Federated Clouds

The proliferation of private clouds that are often underutilized and the tremendous computational potential of these clouds when combined has recently brought forth the idea of volunteer cloud computing (VCC), a computing model where cloud owners contribute underutilized computing and/or storage resources on their clouds to support the execution of applications of other members in the community. This model is particularly suitable to solve big data scientific problems. Scientists in data-intensive scientific fields increasingly recognize that sharing volunteered resources from several clouds is a cost-effective alternative to solve many complex, data- and/or compute-intensive science problems. Despite the promise of the idea of VCC, it still remains at the vision stage at best. Challenges include the heterogeneity and autonomy of member clouds, access control and security, complex inter-cloud virtual machine scheduling, etc. In this paper, we present CloudFinder , a system that supports the efficient execution of big data workloads on volunteered federated clouds (VFCs). Our evaluation of the system indicates that VFCs are a promising cost-effective approach to enable big data science.

Development Trends and Frontiers of Ocean Big Data Research Based on CiteSpace

Modern socio-economic development and climate prediction depend greatly on the application of ocean big data. With the accelerated development of ocean observation methods and the continuous improvement of the big data science, the challenges of multiple data sources and data diversity have emerged in the ocean field. As a result, the current data magnitude has reached the terabyte scale. Currently, the traditional theoretical foundation and technical methods have their inherent limitations and demerits that cannot satisfied the temporal and spatial attributes of the current ocean big data. Numerous scholars and countries were involved in ocean big data research. To explore the focus and current status, and determine the topics of research on bursts and acquisition of trend related to ocean big data, 400 articles between 1990 and 2019 were collected from the “Web of Science.” Combined with visualization software CiteSpace, bibliometrics method and literature combing technology, the pivotal literature related to ocean big data, including significant level countries, institutions, authors, journals and keywords were recognized. A synthetical analysis has revealed research hot spots and research frontiers. The purpose of this study is to provide researchers and practitioners in the field of ocean big data with the main research domains and research hotspots, and orientation for further research.

Harnessing data science to advance radiation oncology.

Radiation oncology, a major treatment modality in the care of patients with malignant disease, is a technology‐ and computer‐intensive medical specialty. As such, it should lend itself ideally to data science methods, where computer science, statistics, and clinical knowledge are combined to advance state‐of‐the‐art care. Nevertheless, data science methods in radiation oncology research are still in their infancy and successful applications leading to improved patient care remain scarce. Here, we discuss data interoperability issues within and across organizational boundaries that hamper the introduction of big data and data science techniques in radiation oncology. At the semantic level, creating common underlying models and codification of the data, including the use of data elements with standardized definitions, an ontology, remains a work in progress. Methodological issues in data science and in the use of large population‐based health data registries are identified. We show that data science methods and big data cannot replace randomized clinical trials in comparative effectiveness research by reviewing a series of instances where the outcomes of big data analyses and randomized trials are at odds. We also discuss the modern wave of machine learning and artificial intelligence as represented by deep learning and convolutional neural networks. Finally, we identify promising research avenues and remain optimistic that the data sources in radiation oncology can be linked to yield important insights in the near future. We argue that data science will be a valuable complement to, but not a replacement of, the traditional hypothesis‐driven translational research chain and the randomized clinical trials that form the backbone of evidence‐based medicine.

Data Science and Graduate Nursing Education: A Critical Literature Review.

The emergence of big data and data science offers unprecedented opportunities for accelerating scientific advances in nursing, yet current nursing curricula are not adequate to prepare students to leverage those opportunities. The purpose of this review was to describe current strategies that can be used to educate graduate nurses about data science methods as well as facilitators and challenges to adopting those strategies. We conducted a critical literature review of papers addressing data science and graduate nursing education. Ten articles were included in this review. The most common strategy was the integration of data science methods into existing courses throughout the graduate nursing curricula. A major facilitator was interdisciplinary collaboration between nursing faculty and colleagues in other disciplines. The findings provide strategies that can be used to prepare graduate nurses to work in data science teams to shape big data research and optimize patient outcomes.