Big Data Approach Research Articles

Big Data analytics for improved prediction of ligand binding and conformational selection.

This research introduces new machine learning and deep learning approaches, collectively referred to as Big Data analytics techniques that are unique to address the protein conformational selection mechanism for protein:ligands complexes. The novel Big Data analytics techniques presented in this work enables efficient data processing of a large number of protein:ligand complexes, and provides better identification of specific protein properties that are responsible for a high probability of correct prediction of protein:ligand binding. The GPCR proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2), OPRD1 (Opioid receptor Delta 1) and OPRK1 (Opioid Receptor Kappa 1) are examined in this study using Big Data analytics techniques, which can efficiently process a huge ensemble of protein conformations, and significantly enhance the prediction of binding protein conformation (i.e., the protein conformations that will be selected by the ligands for binding) about 10-38 times better than its random selection counterpart for protein conformation selection. In addition to providing a Big Data approach to the conformational selection mechanism, this also opens the door to the systematic identification of such "binding conformations" for proteins. The physico-chemical features that are useful in predicting the "binding conformations" are largely, but not entirely, shared among the test proteins, indicating that the biophysical properties that drive the conformation selection mechanism may, to an extent, be protein-specific for the protein properties used in this work.

Who are the gig workers? Evidence from mapping the residential locations of ride-hailing drivers by a big data approach

One of the challenges faced in understanding platform gig work is who are the gig workers involved in this novelty labour relationship. We attempt to answer it by using mobile phone trajectories to map the residential locations of gig drivers in the ride-hailing platform economy in Chengdu, China. Then, we reconstruct the relationships between the spatial structure of the ride-hailing drivers and their neighbourhood-level sociodemographic characteristics and residential built environments. We found that ride-hailing drivers are more likely to come from neighbourhoods with lower-income and less regular job opportunities. Their residences formed living clusters with urban villages, resettlement houses, and shanty towns. Our study suggests that ride-hailing platforms do not liberate gig workers from the structural rural-urban disparities but form a continuation of the structural barriers and discrimination in the division of labour, even if the technological innovation discourse in the platform economy argues that the gig driving can attract the well-educated and other minorities.

Immigration to Croatia: Big (Migration) Data Approach

Immigration to Croatia: Big (Migration) Data Approach

Emerging Load Testing Approach of Big Data & Cloud Computing

Switch over a few utterances by using information technology in a variety of ways assembles big amounts of data. Such data necessitates selling out and storage. The cloud is an online storage space model where data is stock up on numerous virtual servers. Big data processing represents a new face up to in computing, particularly in cloud computing. Data Science dispensation engross data acquirement, storage and analysis. In this respect, there are many questions including, what the connection between big data and Data Science is computing. The answer to these difficulties will be talk about in this paper, where the big data and cloud computing will be studied, in adding up to receiving acquainted with connection between them in the terms of security and confronts. We have suggested a period for big data, and a model that illustrates the relationship between big data and cloud computing.

PS-P02-3: DIETARY RISK FACTORS FOR HYPERTENSION- INSIGHTS FROM THE TESCO 1.0 DATASET

Background: Tesco published an open-source dataset containing area-aggregated product purchase information from 420m transactions across 16m individual Clubcard users in the Greater London area, at a borough level. This dataset is presented as a typical product purchase for each borough, divided into 220 individual datapoints. Purpose: We wanted to explore dietary correlates with the prevalence of hypertension, utilising the London Borough wide Quality and Outcomes Framework (QOF) for GP reported hypertension diagnoses. Methods: The Tesco 1.0 dataset (2015) was accessed on 1/11/21 and all data downloaded. Hypertension data was accessed from the QOF reporting database (gpcontract.co.uk) for the last year available presented by Borough (2017). HYP001: the number of established hypertension patients as a total of registered patients, and HYP006: the % of hypertensive patients with a reading < 150/90 mmHg were selected, representing the prevalence and control of hypertension respectively. To explore for relevant associated factors, the average deprivation index (IMD) for each borough was accessed from Public Health England, and demographic data accessed from the Greater London Authority database. Univariable correlation using Spearmans Rho was calculated in using scipy in Python (3.0) and significance value of < = 0.01. Results: Highly significant risk factors for hypertension were the consumption of sugar, carbohydrate and sweets. Diabetes mellitus and obesity prevalence were also highly correlated with hypertension. Salt consumption was not a significant risk factor. Highly significant protective factors included consumption of eggs, fish, fruit and vegetables. Conclusions: The Big Data approach of utilising supermarket data allows a unique insight into dietary habits at a population level. We have demonstrated here an unexpected finding, the consumption of sugar and carbohydrate are highly significant risk factors for hypertension prevalence, while salt consumption is not. While not the first time the sugar versus salt question has been asked, this is the first time it has been answered in this manner.

Pre- and post-task resting-state differs in clinical populations

Resting-state functional connectivity has generated great hopes as a potential brain biomarker for improving prevention, diagnosis, and treatment in psychiatry. This neuroimaging protocol can routinely be performed by patients and does not depend on the specificities of a task. Thus, it seems ideal for big data approaches that require aggregating data across multiple studies and sites. However, technical variability, diverging data analysis approaches, and differences in data acquisition protocols introduce heterogeneity to the aggregated data. Besides these technical aspects, a prior task that changes the psychological state of participants might also contribute to heterogeneity. In healthy participants, studies have shown that behavioral tasks can influence resting-state measures, but such effects have not yet been reported in clinical populations. Here, we fill this knowledge gap by comparing resting-state functional connectivity before and after clinically relevant tasks in two clinical conditions, namely substance use disorders and phobias. The tasks consisted of viewing craving-inducing and spider anxiety provoking pictures that are frequently used in cue-reactivity studies and exposure therapy. We found distinct pre- vs post-task resting-state connectivity differences in each group, as well as decreased thalamo-cortical and increased intra-thalamic connectivity which might be associated with decreased vigilance in both groups. Our results confirm that resting-state measures can be strongly influenced by prior emotion-inducing tasks that need to be taken into account when pooling resting-state scans for clinical biomarker detection. This demands that resting-state datasets should include a complete description of the experimental design, especially when a task preceded data collection.

A Big Data Approach to Explore Medical Imaging Repositories Based on DICOM

Medical imaging repositories based on the DICOM format such as Picture Archiving and Communication Systems have a huge potential from a big data perspective. The study reported by this paper aimed to verify on how the big data lifecycle processes, from production to consumption might be implemented to take advantage of the DICOM standard and respective data sources. After identifying the different processes (i.e., data collection, integration, filtering, anonymization and enrichment, and knowledge extraction) the study demonstrate their implementation using an open-source application able to access the DICOM metadata independently of the medical imaging modalities and equipment manufacturers.

The role of artificial intelligence in achieving entrepreneurship: A applied study on Al-Nasr Factory in Ibn Majid General Company

Summing up the body of research on artificial intelligence and its potential future paths reveals two dimensions of the rising relevance of AI for entrepreneurship study. In the first place, practical uses of AI become a separate field of study. Dependency on AI-driven research methodologies, including big data approaches or AI-driven prediction techniques, is the second dimension. These AI-based techniques give academics fresh and useful perspectives on entrepreneurs and entrepreneurial endeavors that are challenging to evaluate using conventional techniques. We come to the conclusion that, in order to preserve the fundamental research standards and scientific rigor that safeguard the field's health, standing, and influence, it is imperative that the field embrace AI as a subject and a research methodology with more zeal. The main objective of this study is to develop entrepreneurial business using artificial intelligence study on artificial intelligence is booming in many fields of business and management study, since the changes it is witnessing have a profound impact on business and the economy as a whole. In the realm of entrepreneurship research, however, artificial intelligence has gotten very little attention., Even Nevertheless, a large number of entrepreneurship scholars concur that artificial intelligence will probably have a significant and disruptive impact on entrepreneurship research. Based on the study's findings, the researcher recommended a number of things, including more research on artificial intelligence and entrepreneurship.

All WARC and no playback: The materialities of data-centered web archives research

This paper examines the Web ARChive (WARC) file format, revealing how the format has come to play a central role in the development and standardization of interoperable tools and methods for the international web archiving community. In the context of emerging big data approaches, I consider the sociotechnical relationships between material construction of data and information infrastructures for collecting and research. Analysis is inspired by Star and Griesemer's historical case of the Museum of Vertebrate Zoology which reveals how boundary objects and methods standardization are used to enroll actors in the work of collecting for natural history. I extend these concepts by pairing them with frameworks for studying digital materiality and the representational qualities of data artifacts. Through examples drawn from fieldwork observations studying two data-centered research projects, I consider how the materiality of the WARC format influences research methods and approaches to data extraction, selection, and transformation. Findings identify three modalities researchers use to configure WARC data for researcher needs: using indexes to support search queries, constructing derivative formats designed for certain types of analysis, and generating custom-designed datasets tailored for specific research purposes. Findings additionally reveal similarities in how these distinct methods approach automated data extraction by relying upon the WARC's standardized metadata elements. By interrogating whose information needs are being met and taken into account in the design of the WARC's underlying information representation, I reveal effects on the emerging field of web history, and consider alternative approaches to knowledge production with archived web data.

Big data from small animals: integrating multi-level environmental data into the Dog Aging Project.

Environmental exposures can have large impacts on health outcomes. While many resources have been dedicated to understanding how humans are influenced by the environment, few efforts have been made to study the role of built and natural environmental features on animal health. The Dog Aging Project (DAP) is a longitudinal community science study of aging in companion dogs. Using a combination of owner-reported surveys and secondary sources linked through geocoded coordinates, DAP has captured home, yard and neighbourhood variables for over 40,000 dogs. The DAP environmental data set spans four domains: the physical and built environment; chemical environment and exposures; diet and exercise; and social environment and interactions. By combining biometric data, measures of cognitive function and behaviour, and medical records, DAP is attempting to use a big-data approach to transform the understanding of how the surrounding world affects the health of companion dogs. In this paper, the authors describe the data infrastructure developed to integrate and analyse multi-level environmental data that can be used to improve the understanding of canine co-morbidity and aging.

Big Data Technology Architecture Proposal for Smart Agriculture for Moroccan Fish Farming

As the global population increases rapidly, so does the need for fishing products. Aquaculture is well-developed in Asian countries but is underdeveloped in countries that share Morocco's climate. To meet the rising demands for aquaculture production, it is vital to embrace new digital strategies to manage the massive amount of data generated by the aquaculture environment. By employing Big Data methodologies, aquaculture activity is handled more effectively, resulting in increased production and decreased waste. This phase enables fish farmers and academics to obtain valuable data, increasing their productivity. Although Big Data approaches provide numerous benefits, they have yet to be substantially implemented in agriculture, particularly in fish farming. Numerous research projects investigate the use of Big Data in agriculture, but only some offer light on the applicability of these technologies to fish farming. In addition, no research has yet been undertaken for the Moroccan use case. This study aims to demonstrate the significance of investing in aquaculture powered by Big Data. This study provides data on the situation of aquaculture in Morocco in order to identify areas for improvement. The paper then describes the adoption of Big Data technology to intelligent fish farming and proposes a dedicated architecture to address the feasibility of the solution. In addition, methodologies for data collecting, data processing, and analytics are highlighted. This article illuminates the possibilities of Big Data in the aquaculture business. It demonstrates the technological and functional necessity of incorporating Big Data into traditional fish farming methods. Following this, a concept for an intelligent fish farming system based on Big Data technology is presented.

Efficiency Assessment of Fully Mechanized Harvesting System through the Use of Fleet Management System

Nowadays the spread of precision forestry has led to the possibility of collecting data related to forest machines for an extended period and with enough precision to support decisions in the optimization of harvesting strategies in terms of technological and environmental efficiency. This study aims to evaluate the effective benefit of automatic data collection through the fleet management system (FMS) of two forest harvesters and two forwarders in pine forests in Poland. The study also aims to determine how the use of FMS can help forest companies to manage their fleet and take advantage of long-term monitoring. Focusing on performance indicators of fuel consumption and CO2 emissions, as well as on the engine parameters from the Can Bus data, the exploration of data was performed following a Big Data approach, from the creation of an aggregate dataset, pre-elaboration (data cleaning, exploration, selection, etc.) using GIS and R software. The investigation has considered the machine productivity, in the case of the harvesters, and the specific fuel consumption of each machine studied, as well as the time used by each of them during the different working cycle activities and the total amount of timber processed. The main results indicate an average emission of 2.1 kg of CO2 eq/m3 for the harvesters and 2.56 kg of CO2 eq/m3 for the forwarders, which equates in total to 0.24% of the carbon stored in one cubic meter of wood.

Translating Big Data to Clinical Outcomes in Anxiety: Potential for Multimodal Integration.

This review describes approaches to research on anxiety that attempt to link neural correlates to treatment response and novel therapies. The review emphasizes pediatric anxiety disorders since most anxiety disorders begin before adulthood. Recent literature illustrates how current treatments for anxiety manifest diverse relations with a range of neural markers. While some studies demonstrate post-treatment normalization of markers in anxious individuals, others find persistence of group differences. For other markers, which show no pretreatment association with anxiety, the markers nevertheless distinguish treatment-responders from non-responders. Heightened error related negativity represents the risk marker discussed in the most depth; however, limitations in measures related to error responding necessitate multimodal and big-data approaches. Single risk markers show limits as correlates of treatment response. Large-scale, multimodal data analyzed with predictive models may illuminate additional risk markers related to anxiety disorder treatment outcomes. Such work may identify novel targets and eventually guide improvements in treatment response/outcomes.

Real world clinical perspective on PDE5i utility in dementia.

AbstractBackgroundThere is clearly a large unmet need for more drug treatments in dementia due to Alzheimer pathology (AD) and in VCID. Mechanistic studies in rodents and in vitro support a benefit of PDE5 inhibitors in AD.MethodA Big data approach recently confirmed this potential benefit with an independent study incorporating multiple levels of biology with in silico network drug discovery for amyloid and tau endophenotypes, data mining from insurance records, as well as in vitro modeling using derived induced pluripotent stem cell (iPSC) neuronal models of AD.ResultsCollectively, this body of work is cause of optimism. However, a real‐world randomized clinical trial (RCT) of the PDE5i tadalafil in older people, incorporating biomarkers, detected no significant effect with respect to cerebral blood flow.ConclusionThese results raise multiple questions for the dementia clinician‐scientist. Do in vitro and in vivo preclinical paradigms provide non‐translatable findings because these systems are not capturing a key component of AD, such as human aging? Is it still worth pursuing PDE5i as a therapeutic route? What could be some factors that contributed to the negative RCT results and what should be emphasized in the design of a definitive RCT for this class of therapeutics? Finally, what role do biomarkers play in filling critical gaps in knowledge to bridge the path between bench and bedside.

Big Data Classification in IOT Healthcare Application Using Optimal Deep Learning

The Internet of Things (IoT) system is composed of several numbers of sensor nodes and systems, which are wirelessly interlinked to the internet. Generally, big data is the storage of a huge amount of information, which causes the classification process to be very challenging. Numerous big data classification approaches are implemented, but the computational time and secure handling of information are the major problems. The aim of the study is the development of big data approach in Internet of Things (IoT) healthcare application. Hence, this paper presents the proposed Dragonfly Rider Competitive Swarm Optimization-based Deep Residual Network (DRCSO-based DRN) for big data classification in IoT. First, the IoT nodes are simulated, and the heart disease patient data are collected through sensors. The routing is done using the Multi-objective Fractional Gravitational Search Algorithm (Multi-objective FGSA). In the Base Station (BS), the big data classification is done. Here, the classification is done using MapReduce (MR) framework, which includes two phases, like mapper and the reducer phase. The input data is initially fed to the mapper phase in the map-reduce (MR) framework. In the mapper phase, feature selection is carried out based on Dragonfly Rider Optimization Algorithm (DROA) in order to select the appropriate features for further processing. The DROA is modeled through merging Dragonfly Algorithm (DA) and Rider Optimization Algorithm (ROA). In the reducer phase, the classification is performed using DRN, which is trained by the developed DRCSO algorithm. The DRCSO is modeled by incorporating DA, ROA and Competitive Swarm Optimization (CSO). In addition, the performance of the developed method is outperformed than the existing approaches such as Linguistic Fuzzy Rules with Canopy Mapreduce (LFR-CM) + Fuzzy classifier, Machine learning-dependent k-nearest neighbors (FML-KNN), MapReduce-Fuzzy Integral-dependent Ensemble Learning Model+Single hidden layer feedforward neural network (MR-FI-ELM + SLFN) and DROA-recurrent neural network (RNN) based on the accuracy, average residual energy and throughput with the value of 0.929, 0.086[Formula: see text]J and 86.585. The proposed method is used to manage and derive meaningful information from the patient’s medical records, medical examinations results and hospital records.

Improved multi-class classification approach for imbalanced big data on spark

Improved multi-class classification approach for imbalanced big data on spark

Data-Driven Endpoint Selection in Data-Poor Scenarios: Bioassay Design for Shale Gas Flowback and Produced Waters

Big data approaches have greatly improved scientific decision making, but they are highly dependent on the availability of data, impeding their use in data-poor scenarios. In addition to data abundance, enhancing data diversity is likewise a way to access knowledge. Herein, we propose a data-driven method for toxicity endpoint selection when directly relevant data are deficient, and shale gas exploitation sites were used as an example scenario. From the 1173 substances in the U.S. Environmental Protection Agency’s HFList, the most concerning endpoints in zebrafish embryo toxicity tests (FET) were inferred using a newly developed relational database (RDB) strategy that integrated chemical, high-throughput screening (HTS) bioactivity, genome, and FET endpoint information. This RDB strategy based on text mining and data fusion approaches enabled the integration of 255 bioactive contaminants, 955 HTS bioassays with known modes of action (MoAs), 214 gene ontologies, 65 pathways, and 27 phenotypic data and predicted measurement endpoints within 10 MoAs for shale gas pollution. This data-driven approach was further validated using zebrafish FET and transcriptomic sequencing with field-collected samples and achieved 89% and 97% accuracy for the predictive ontologies and pathways, respectively. This highlighted the applicability of RDB-based data-driven strategies for predicting toxicity endpoints from a priori knowledge of contaminants by improving data diversity.

Development of a Safety Prediction Method for Arterial Roads Based on Big-Data Technology and Stacked AutoEncoder-Gated Recurrent Unit

Modern complexities associated with an arterial traffic makes existing safety prediction methods insufficient to meet desired standards required by recent developmental needs. This paper proposes an enhanced active safety prediction method based on big-data approach and Stacked AutoEncoder-Gated Recurrent Unit. Firstly, the big-data technology is used to construct a dynamic identification model to recognize real-time operation state and risk state. Secondly, the Stacked AutoEncoder-Gated Recurrent Unit is used to predict a level of safety based on associated recognition results. This paper uses data from working days of Sunset Boulevard, California, from January <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1^{\mathrm{st}}$ </tex-math></inline-formula> , 2020, to February <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$28^{\mathrm{th}}$ </tex-math></inline-formula> , 2020. The results of analysis show that the accuracy of the proposed dynamic recognition model reaches 98.92%, which is better than existing models such as random forest, K-nearest neighbor, and naïve Bayes models. In addition, it is found that the Stacked AutoEncoder-Gated Recurrent Unit can achieve a prediction accuracy of 95.157% and has significant advantages in terms of efficiency. The proposed methods will provide feasible solutions for actively monitoring safety levels.

Big data approaches to knot theory: Understanding the structure of the Jones polynomial

In this paper, we examine the properties of the Jones polynomial using dimensionality reduction learning techniques combined with ideas from topological data analysis. Our data set consists of more than 10 million knots up to 17 crossings and two other special families up to 2001 crossings. We introduce and describe a method for using filtrations to analyze infinite data sets where representative sampling is impossible or impractical, an essential requirement for working with knots and the data from knot invariants. In particular, this method provides a new approach for analyzing knot invariants using Principal Component Analysis. Using this approach on the Jones polynomial data, we find that it can be viewed as an approximately three-dimensional subspace, that this description is surprisingly stable with respect to the filtration by the crossing number, and that the results suggest further structures to be examined and understood.

A big data szerepe a városi mobilitás kutatásában és fejlesztésében

The use of big data in urban mobility research is growing dynamically. However, relatively few studies have been devoted to systematically review the results in this field. Therefore, the aim of the present study is to explore the general characteristics of the application of the big data approach in urban mobility research. For this purpose, the systematic literature review method was applied and the scientific Scopus database was accessed to collect publications relevant to the predefined criteria. Then the papers included in the sample were analysed using quantitative and qualitative techniques, focusing on the trends emerging from the years of publications in the database, the distribution of the papers by journal, the geographical distribution of case-study areas, the methods and data types employed, and their policy implications. This literature review demonstrates the diversity of big datadriven urban mobility research and provides lessons for geography and urban policy in Hungary.