Analysis Of Big Datasets Research Articles

Big data and experimental biology: the complementary roles of hypothesis-led and blue skies research.

JEB has broadened its scope to include non-hypothesis-led research. In this Perspective, based on our lab's lived experience, I argue that this is excellent news, because truly novel insights can occur from 'blue skies' idea-led experiments. Hypothesis-led and hypothesis-free experimentation are not philosophically antagonistic; rather, the latter can provide a short-cut to an unbiased view of organism function, and is intrinsically hypothesis generating. Insights derived from hypothesis-free research are commonly obtained by the generation and analysis of big datasets - for example, by genetic screens - or from omics-led approaches (notably transcriptomics). Furthermore, meta-analyses of existing datasets can also provide a lower-cost means to formulating new hypotheses, specifically if researchers take advantage of the FAIR principles (findability, accessibility, interoperability and reusability) to access relevant, publicly available datasets. The broadened scope will thus bring new, original work and novel insights to our journal, by expanding the range of fundamental questions that can be asked.

Islamic Perspectives on Polygenic Testing and Selection of IVF Embryos (PGT-P) for Optimal Intelligence and Other Non-Disease-Related Socially Desirable Traits.

In recent years, the genetic testing and selection of IVF embryos, known as preimplantation genetic testing (PGT), has gained much traction in clinical assisted reproduction for preventing transmission of genetic defects. However, a more recent ethically and morally controversial development in PGT is its possible use in selecting IVF embryos for optimal intelligence quotient (IQ) and other non-disease-related socially desirable traits, such as tallness, fair complexion, athletic ability, and eye and hair colour, based on polygenic risk scores (PRS), in what is referred to as PGT-P. Artificial intelligence (AI) and machine learning-based analysis of big data sets collated from genome sequencing of specific human ethnic populations can be used to estimate an individual embryo's likelihood of developing such multifactorial traits by analysing the combination of specific genetic variants within its genome. Superficially, this technique appears compliant with Islamic principles and ethics. Because there is no modification of the human genome, there is no tampering with Allah's creation (taghyīr khalq Allah). Nevertheless, a more critical analysis based on the five maxims of Islamic jurisprudence (qawa'id fiqhiyyah) that are often utilized in discourses on Islamic bioethics, namely qaṣd (intention), yaqın̄ (certainty), ḍarar (injury), ḍarūra (necessity), and `urf (custom), would instead reveal some major ethical and moral flaws of this new medical technology in the selection of non-disease-related socially desirable traits, and its non-compliance with the spirit and essence of Islamic law (shariah). Muslim scholars, jurists, doctors, and biomedical scientists should debate this further and issue a fatwa on this new medical technology platform.

An Analysis of Innovative Development Based on Computer Assisted in Promoting the International Transmission of Chinese Culture under the Background of Big Data

ABSTRACT The study of Chinese culture by foreign scholars is becoming increasingly prevalent, and as a result, there is a growing need to find new and innovative ways to promote the transmission of Chinese culture. To this end, this study proposes using computer-aided technology and big data analysis to analyze existing cultural communication methods and optimize communication programs. The use of big data analysis can provide valuable insights into the preferences and needs of different audiences, which can be used to tailor communication strategies to be more effective. Additionally, computer-aided technology can be used to create engaging and interactive experiences for audiences, further enhancing the effectiveness of cultural communication efforts. One of the key objectives of this study is to develop a series processing mode that optimizes the program of communication for scientific guidance. To achieve this, the researchers will conduct adaptability analysis from the perspective of computer technology in the context of big data. By applying computer-aided technology, the research team will analyze existing methods of cultural communication and optimize them for maximum effectiveness. To ensure that the optimized programs are effective and respectful of the promoted culture, the researchers will work closely with experts and members of the cultural community. This will help to ensure that communication efforts are appropriate and culturally sensitive. The results of this study will be the development of an analysis of big data sets that can be used to guide the promotion of Chinese culture to a wider audience. To test the feasibility of the optimized programs, the researchers will conduct a series of innovative program tests. Ultimately, this study aims to create more effective and engaging programs that can help share the richness and diversity of Chinese culture with a wider audience.

Using Big Data by Ukrainian official statistics when martial law applies: problems and solutions

The article is focused on issues of the secure operation of official statistics in Ukraine during the application of martial law. It was found that the gaps in conventional sources of statistical data caused by the war needed to be filled with data from alternative sources, including Big Data. The level of digitalisation in Ukraine as the basis for using Big Data was analysed by the proposed indices of internetisation, social progress and digital transformation. Thanks to our research, several problems (methodological, legal, financial, and managerial) were identified as vital for statistical offices on their way to the implementation of Big Data in statistical processes. Our proposals concern tools for Big Data processing, such as Data Hypercube as a way for presenting Big Data for their visualisation, applications of Web scraping in estimating the consumer prices index, analyses of labour and real estate markets, and the applications of specialised software for the collection, processing and analysis of Big Data sets.

The Impact of Wearable Technologies in Health Research: Scoping Review.

BackgroundWearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.ObjectiveIn this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.MethodsWe performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing.ResultsWe included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations—wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).ConclusionsWearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data.

BackgroundThe COVID-19 pandemic has impacted people’s lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States.ObjectiveWe aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with—or precede—real-life events?MethodsWe analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states.ResultsThe data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor’s appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others.ConclusionsCOVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

A New Measure of Complementarity in Market Basket Data

Modern IT systems collect detailed data on each activity, transaction, forum entry, conversation and many other areas. The availability of large data volumes in the business, industry and research fields opens up new opportunities for the empirical verification of various economic theories and laws. The analysis of big datasets in turn allows us to look at many issues from a new point of view and see the dependencies that are otherwise difficult to derive. In this paper, we propose a new measure for dependencies between goods in market basket data. The introduced measure was inspired by the well-known microeconomic concept of complementarity. Due to its similar properties to those of complementarity, the new measure was called basket complementarity (b-complementarity). B-complementarity not only measures the strength of dependencies between goods but also measures the direction of these dependencies. The values of the proposed measure can be relatively easily calculated using market basket data. This paper also presents a simple example illustrating this new concept, areas of possible application (e.g., in e-commerce) and preliminary results of searching for goods that meet the criteria of basket complementarity in real market basket data.

A New Measure of Complementarity in Market Basket Data

Modern IT systems collect detailed data on each activity, transaction, forum entry, conversation and many other areas. The availability of large data volumes in the business, industry and research fields opens up new opportunities for the empirical verification of various economic theories and laws. The analysis of big datasets in turn allows us to look at many issues from a new point of view and see the dependencies that are otherwise difficult to derive. In this paper, we propose a new measure for dependencies between goods in market basket data. The introduced measure was inspired by the well-known microeconomic concept of complementarity. Due to its similar properties to those of complementarity, the new measure was called basket complementarity (b-complementarity). B-complementarity not only measures the strength of dependencies between goods but also measures the direction of these dependencies. The values of the proposed measure can be relatively easily calculated using market basket data. This paper also presents a simple example illustrating this new concept, areas of possible application (e.g., in e-commerce) and preliminary results of searching for goods that meet the criteria of basket complementarity in real market basket data.

Методы панорамной визуализации и цифрового анализа теплофизических полей. Обзор.

The paper presents a review of modern methods in the registration, processing, and analysis of dynamic processes in liquids, gases, plasmas, multiphase media, which are realized in researches and technology. The review author observes both the physical fundamentals of flow visualization and the basics of modern technologies for the digital processing of recorded flow images. A brief analysis of the panoramic visualization methods progress history covers a period of one and a half centuries. In the works of the last decade, the focus is on the methods of computer processing, tools, technologies for analyzing and recognizing panoramic thermophysical fields, which make it possible to obtain quantitative information about flows. The review contains an analysis of publications describing the main modern methods of visualization of flows: methods based on the phenomenon of refraction; electroluminescence; digital tracing (particle image velocimetry), visualization of surface flows (PSP and TSP coatings, liquid crystals; oil coatings). Particular attention is paid to methods using cross-correlation image processing algorithms. Those are: digital tracing (PIV), shadow background method (BOS), seedless shadow methods, thermographic PIV, velocity measurement in viscous coatings, micro, tomographic modifications of PIV, etc. The actual problem of digital data analyzing in a panoramic experiment is touched upon - the problem of big data. Examples of machine learning use in the analysis of big data sets of shadow surveys are given. Some examples of numerical simulation data visualization (simulation of experimental flowfields) are considered.

Effects of Viticultural Mechanization on Working Time Requirements and Production Costs

The purpose of this study was to empirically estimate machine and labor costs for mechanization of viticultural processes and to assess the relative effect of mechanization options on viticultural costs. By identifying three external factors that determined the degree of optimal mechanization of three major viticultural processes, we developed a systematic typology of 12 unique vineyard types. To estimate the effects of the factors and processes on production costs, we analyzed the variance of more than 3400 single working time records of five German wine estates across three years with fixed and random effects. Mechanization of general viticultural processes, harvesting, and pruning strongly affected viticultural costs. Minimal pruning provided a cost savings potential of 58% for vineyards that permitted mechanized harvesting. Very steep slopes suffered significant cost disadvantages of up to 164% that could only partially be compensated by mechanization. We identified opportunities for wine producers to improve their economic profitability by increasing viticultural mechanization. The methodological framework developed can be applied to other wine regions with different cost settings and the analysis of big data sets from digitalization of viticulture. The results can aid wine producers and policymakers to choose cost-efficient viticultural systems and provide benchmarks to compare labor intensity.

Моделирование распространения видов секции Xerobia Bunge рода Oxytropis DC. на территории Центральной Азии при климатических изменениях в прошлом и будущем

Modern botanical studies revealing patterns of plant species distribution are based on analysis of big datasets. Despite publishing many maps of diversity and species richness on the global scale and for huge biogeographic regions of the world, the territories of Northern and Central Asia remain poorly studied. We elaborated a special database, including distribution of 19 Oxytropis species of the section Xerobia with 1353 localitites (See Fig. 1). For all species, we analyzed their whole distribution range, including data from Kazakhstan, Mongolia and China. Species distribution was detected according to the main foreign and Russian herbaria, online databases, field data and relevés. Species distribution modeling was performed using Maxent 3.3.3k with MIROC-ESM model in resolution of 2.5 arc-minutes. 19 BIOCLIM and 18 ENVIREM variables were analyzed. Past climate change was evaluated using ENVIREM variables for the Mid-Holocene (ca. 6.000 yr. BP) and the Last Glacial Maximum (ca. 22.000 yr. BP). Future distribution modeling was carried out basing on different climatic scenarios, according to IPCC AR5: RCP8.5, RCP2.6 and RCP6.0. Species distribution from the section Xerobia mostly occupied the territory of Central Asia (See Fig. 1). Few species, such as Oxytropis grandiflora (Pall.) DC. and O. leptophylla (Pall.) DC., were mostly found in the western part of Xerobia section distribution on the territory of Zabaykal’skiy region of Russia, Eastern province of Mongolia and Inner Mongolia province of China. The most part of Xerobia species have isolated distribution and often occupy specific habitats. In such case, using SDM with only bioclimatic variables for local endemic species is pointless. So, we chose species Oxytropis ampullata (Pall.) Pers. (See Fig. 2) with Central Asian distribution and O. grandiflora with Manchuro-Dahurian distribution for modeling (See Fig. 3A). The selected species differ in their ecology: O. ampullata is a mountainous species, whereas most habitats for O. grandiflora are river valleys and mid-mountainous regions. Our analysis showed that ENVIREM variables provide more correct modeling results than BIOCLIM variables (See Fig. 2). Predictive maps on the basis of BIOCLIM variables showed wide potential distribution for O. ampullata, which does not correspond well to the species ecology. The main habitats for this species are such mountainous regions as the Khangai mountains, the Russian and the Mongolian Altai mountains, the Dzhungarian mountains, and the Tarbagatai ridge. Additionally, modeling showed potential distribution for the species in the Selenga river valley. Modern distribution of O. grandiflora was studied quite well; suitable habitats with new localities for the species can be found in the Khentii mountains (See Fig. 3A). The determinants for O. ampullata are mean annual temperature, isothermality and potential evapotranspiration (PET) of the driest and coldest quarter (See Table 1). PET parameters in the driest and the coldest time of the year have the key meaning because in arid conditions plants receive the main portion of moisture in the colder period when the evaporation is not intense, also it is important to conserve the moisture during the dry season. Distribution of O. grandiflora is limited by temperature and precipitation seasonality, temperature annual range, PET seasonality, and PET of the driest quarter (See Table 1). Determinants for the species with Central-Asian distribution O. ampullata are connected with temperature variables, whereas for Manchuro-Daurian species O. grandiflora precipitation matters (See Table 1 and Fig. 3A). The key factors for modern distribution of the studied Xerobia species are mean monthly potential evapotranspiration of the driest quarter, continentality index and climatic moisture index (See Fig. 3B). All these variables were determinants for the mid-Holocene and the Last Glacial Maximum (See Table 2 and Fig. 4), which might give evidence of relatively stable environmental conditions in the studied region. Central Asia has not been severely affected by glaciation as more northern latitudes and climate conditions on that territory were relatively stable during a long period. Modeling for the past climate showed a wider distribution for Xerobia species in the north-west during the Last Glacial Maximum and future shrinking during the Mid-Holocene till modern time (See Fig. 4). The north-eastern territories, such as Zabaykal’skiy region of Russia and, partially, the central part of Siberia, are characterized by a wider distribution under modern climate conditions. Species habitats of that territory are mostly confined with mountains. It is consistent with previous studies that described Southern Siberia as one of the centers of speciation for the genus Oxytropis. This region has now high Oxytropis species richness with a great number of endemics.Predictive maps for different climate scenarios reveal insignificant changes in distribution of the section Xerobia, even for the maximum climate warming (RCP8.5 scenario) (See Fig.5). Under predicted climate change, potential habitats in the southwest and in the north-east of Xerobia distribution, as well as a slight shrinking in the south-east can be observed in the future.

Homomorphic Encryption: The ‘Holy Grail’ for Big Data Analytics and Legal Compliance in the Pharmaceutical and Healthcare Sector?

The pharmaceutical and healthcare sector is a prime target for cybercriminals around the world. These cyber-attacks represent significant challenges in the context of data protection and data security. The General Data Protection Regulation (GDPR) imposes strict rules regarding the processing and analysis of personal data. In conventional approaches, data analysts request data from various sources. Then, they anonymise or pseudonymise the data using various tools and techniques. These methods often use powerful algorithms to ensure a high level of security. However, these methods tend to either reduce the quality of data for further analysis or they expose the data while decrypting it for analysis. Homomorphic Encryption (HE) has recently been touted as the ‘Holy Grail’ of cryptography since it allows the analysis of big data sets without ever needing to decrypt and thus compromising the confidentiality of the data. This provides a whole new layer of protection and at the same time allows the processing of data for secondary use and scientific research. While HE is not a new technology, it is still in the early stages of development. In this piece, we will introduce a new automated tool for searching and analysing encrypted data using HE techniques, which is being developed within the scope of the EnergyShield project. (Less)

Perturbation theory for Markov chains via Wasserstein distance

Perturbation theory for Markov chains addresses the question of how small differences in the transition probabilities of Markov chains are reflected in differences between their distributions. We prove powerful and flexible bounds on the distance of the nth step distributions of two Markov chains when one of them satisfies a Wasserstein ergodicity condition. Our work is motivated by the recent interest in approximate Markov chain Monte Carlo (MCMC) methods in the analysis of big data sets. By using an approach based on Lyapunov functions, we provide estimates for geometrically ergodic Markov chains under weak assumptions. In an autoregressive model, our bounds cannot be improved in general. We illustrate our theory by showing quantitative estimates for approximate versions of two prominent MCMC algorithms, the Metropolis-Hastings and stochastic Langevin algorithms.

Intelligent Transportation System (ITS) for Smart-Cities using Mamdani Fuzzy Inference System

It is estimated that more than half of the world population lives in cities according to (UN forecasts, 2014), so cities are vital. Cities, as we all know facing with complex challenges – for smart cities the outdated traditional planning of transportation, environmental contamination, finance management and security observations are not adequate. The developing framework for smart-city requires sound infrastructure, latest current technology adoption. Modern cities are facing pressures associated with urbanization and globalization to improve quality-of-life of their citizens. A framework model that enables the integration of cloud-data, social network (SN) services and smart sensors in the context of smart cities is proposed. A service-oriented radical framework enables the retrieval and analysis of big data sets stemming from Social Networking (SN) sites and integrated smart sensors collecting data streams for smart cities. Smart cities’ understanding is a broad concept transportation sector focused in this article. Fuzzification is shown to be a capable mathematical approach for modelling traffic and transportation processes. To solve various traffic and transportation problems a detailed analysis of fuzzy logic systems is developed. This paper presents an analysis of the results achieved using Mamdani Fuzzy Inference System to model complex traffic processes. These results are verified using MATLAB simulation.

Tomorrow’s Health Data Scientists in the Making: 2 Years of Training &amp; Capacity Building Activities with the Farr Institute

ABSTRACT&#x0D; ObjectivesThe poster will showcase the Training &amp; Capacity Building Programme (TCBP) established by the Farr network that is available to PhD students and Early Career Researchers (ECRs) both from within and outside the network.&#x0D; ApproachThe authors are using a mixed-methods approach to present the current state of the art that has motivated the structure of the TCBP and how the programme has been received by students, academic and industry leaders.&#x0D; Since 2014, the Farr network education leads have worked on a unique education programme that aims to equip students with key professional and methodological skills required by the roles they are likely to take in their future careers.&#x0D; The content of the programme has been informed by current research in the fields of data linkage and analysis of big datasets as well as the immediate experience of researchers, and healthcare and industry leads working actively in the field.&#x0D; We have focussed on developing a community of practice through the provision of an environment that has enabled students to share experiences and knowledge and to start building their own networks of collaboration across the Farr centres.&#x0D; ResultsThe Farr network of PhD students comprises of approximately 66 students from a variety of backgrounds including bioinformatics, computer science, epidemiology, mathematics, psychology and statistics.&#x0D; The Farr network hosted 2 main events in 2015 that provided the foundation for 34 PhD students to come together to develop skills in written and oral communication, Public and Patient Involvement (PPI), problem-solving, requirements gathering, data visualisation and spatial data analysis.&#x0D; Formal feedback collected from each event suggests that the programme is received well by students who see it as a set of activities that complement their specialised PhD related education. Feedback from supervisors and future employers also suggests that this programme has facilitated the development of skills that are useful for their PhD and employment.&#x0D; ConclusionThe Farr network has laid the foundation for the community of practice around the analysis of real-world health datasets across the UK, and will continue to cultivate this through the enrichment of the programme.

Minireview: Multiomic candidate biomarkers for clinical manifestations of sickle cell severity: Early steps to precision medicine.

In this review, we provide a description of those candidate biomarkers which have been demonstrated by multiple-omics approaches to vary in correlation with specific clinical manifestations of sickle cell severity. We believe that future clinical analyses of severity phenotype will require a multiomic analysis, or an omics stack approach, which includes integrated interactomics. It will also require the analysis of big data sets. These candidate biomarkers, whether they are individual or panels of functionally linked markers, will require future validation in large prospective and retrospective clinical studies. Once validated, the hope is that informative biomarkers will be used for the identification of individuals most likely to experience severe complications, and thereby be applied for the design of patient-specific therapeutic approaches and response to treatment. This would be the beginning of precision medicine for sickle cell disease.

From human mobility to renewable energies

We address and discuss recent trends in the analysis of big data sets, with the emphasis on studying multiscale phenomena. Applications of big data analysis in different scientific fields are described and two particular examples of multiscale phenomena are explored in more detail. The first one deals with wind power production at the scale of single wind turbines, the scale of entire wind farms and also at the scale of a whole country. Using open source data we show that the wind power production has an intermittent character at all those three scales, with implications for defining adequate strategies for stable energy production. The second example concerns the dynamics underlying human mobility, which presents different features at different scales. For that end, we analyze 12-month data of the Eduroam database within Portuguese universities, and find that, at the smallest scales, typically within a set of a few adjacent buildings, the characteristic exponents of average displacements are different from the ones found at the scale of one country or one continent.