Big Domain Research Articles

Big Data Storage Tools Using NoSQL Databases and Their Applications in Various Domains: A Systematic Review

Over the past few years, data has been growing significantly due to the advent of new connected devices, availability of bandwidth, and the emergence of new applications which utilize cloud computing infrastructure in the data centers. This increased amount of data faces many problems in terms of storage, transmission, management, and processing, etc. Therefore, the term big data has gained significant attention from researchers in recent years. The rapidly growing quantity, velocity, and variety of data require more probable and logical tools for its storage. For this purpose, the industry is highly emphasizing the development of more viable tools for the storage of big data. The traditional big data storage tools are unsuccessful in storing an enormous amount of data. Hence, the structural modifications of management mechanisms of conventional storage systems such as SQL databases to NoSQL databases technology are necessary to cope up with drastically increasing requirements of big data storage. The primary objective of this paper is to concentrate exclusively on designing a road map for NoSQL big data storage technologies, evaluate current evidence, research progresses in NoSQL data storage systems and their applications in various domains. We conducted a systematic literature review (SLR) of various studies published in recent years. We propose a framework to classify selected articles on the basis of various factors such as motivations behind big data storage, NoSQL techniques used for storing big data, and significant applications of big data in different domains. Furthermore, we also discuss research issues and define an outline for future research in the big data storage domain for NoSQL databases.

Kognitor: Big Data Real-Time Reasoning and Probabilistic Programming

There is a huge increase in the amount of generated data since the explosion of the Internet. This generated data which is usually collected in different formats and from multiple sources is popularly termed Big Data. Big data contains uncertainty. To handle uncertainty in big data, probabilistic reasoning is used to develop probabilistic models that specify generic knowledge in different topics. These models are used in conjunction with an inference algorithm to enable decision makers especially during uncertain situations. Extensive knowledge in fields such as statistics, machine learning and probability theories are employed in the development of these probabilistic models. Thus, it is usually a difficult undertaking. Probabilistic programming was introduced to simplify and enable development of complex models. Again, decision makers often need to use knowledge from historic data as well as current data to make cogent decisions. Thus, the necessity to unify processing of historic and real-time data with low latency. The Lambda architecture was introduced for this purpose. This paper presents a framework called Kognitor that simplifies the design and development of difficult models using probabilistic programming and Lambda architecture. Evaluation of this framework is also presented in this paper using a case study to highlight the crucial potential of probabilistic programming to achieve simplification of model development and enable real-time reasoning on big data. Thus, demonstrating the effectiveness of the framework. Finally, results of this evaluation are presented in this paper. The Kognitor framework can be used to steer effective and easier implementation of complicated real-life situations as probabilistic models. This will be beneficial in the big data processing domain and for decision makers. Kognitor ensures cost-effectiveness using contemporary big data tools and technology on commodity hardware. Kognitor framework will also be beneficial in academia with respect to the use of probabilistic programming.

The extracellular juncture domains in the intimin passenger adopt a constitutively extended conformation inducing restraints to its sphere of action

Enterohemorrhagic and enteropathogenic Escherichia coli are among the most important food-borne pathogens, posing a global health threat. The virulence factor intimin is essential for the attachment of pathogenic E. coli to the intestinal host cell. Intimin consists of four extracellular bacterial immunoglobulin-like (Big) domains, D00–D2, extending into the fifth lectin subdomain (D3) that binds to the Tir-receptor on the host cell. Here, we present the crystal structures of the elusive D00–D0 domains at 1.5 Å and D0–D1 at 1.8 Å resolution, which confirms that the passenger of intimin has five distinct domains. We describe that D00–D0 exhibits a higher degree of rigidity and D00 likely functions as a juncture domain at the outer membrane-extracellular medium interface. We conclude that D00 is a unique Big domain with a specific topology likely found in a broad range of other inverse autotransporters. The accumulated data allows us to model the complete passenger of intimin and propose functionality to the Big domains, D00–D0–D1, extending directly from the membrane.

Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients Using Primary Care Electronic Health Records.

A growing elderly population suffering from incurable, chronic conditions such as dementia present a continual strain on medical services due to mental impairment paired with high comorbidity resulting in increased hospitalization risk. The identification of at risk individuals allows for preventative measures to alleviate said strain. Electronic health records provide opportunity for big data analysis to address such applications. Such data however, provides a challenging problem space for traditional statistics and machine learning due to high dimensionality and sparse data elements. This article proposes a novel machine learning methodology: entropy regularization with ensemble deep neural networks (ECNN), which simultaneously provides high predictive performance of hospitalization of patients with dementia whilst enabling an interpretable heuristic analysis of the model architecture, able to identify individual features of importance within a large feature domain space. Experimental results on health records containing 54,647 features were able to identify 10 event indicators within a patient timeline: a collection of diagnostic events, medication prescriptions and procedural events, the highest ranked being essential hypertension. The resulting subset was still able to provide a highly competitive hospitalization prediction (Accuracy: 0.759) as compared to the full feature domain (Accuracy: 0.755) or traditional feature selection techniques (Accuracy: 0.737), a significant reduction in feature size. The discovery and heuristic evidence of correlation provide evidence for further clinical study of said medical events as potential novel indicators. There also remains great potential for adaption of ECNN within other medical big data domains as a data mining tool for novel risk factor identification.

PROMOTING NANOSCIENCE TOPICS IN FORMAL EDUCATION

In general, considering a reasonable and relative periodicity, the school curriculum has to pass a process of updating and/or reform - in many cases, in strong connection to the nature and extent of the changes that occurred in each educational system, but also linked with what is happening in the society. Nowadays, many opportunities for getting knowledge and developing personal and social affirmation of each individual are related to the spectacular advancement of science and technology. In the actual context, significant changes are noticed in the labor market, pushing the educational systems to anticipate the needs of the near-future society, not just to react to technological developments. In this respect, creativity, entrepreneurial and managerial skills, responsible use of technology, responsible research and innovation, are already present in the education of the 21st century. In this respect, nano-education has been introduced and developed in Romania, and recording an entire process of consolidation, especially at the level of higher education - promoting nano-area as one of the most dynamic big domains in the world of research since the beginning of the century. However, in secondary education, the preoccupations remain sporadic, several positive aspects being recorded especially in non-formal education. This paper tries to offer an answer related to how important nano-education is for the actual generation of youngers, and what are the main problems that teachers face when trying to promote aspects linked with the nano-world.

Parametric Analysis of Resource Management in HDFS

Data has become an integral part of human life in the modern world. As there is increase in volume and different varieties of data available, big data domain has become a powerful area. Big data uses more resources for the processing. This paper focuses on resources utilized in big data environment to perform the operations. It uses various resources like memory, files and network bandwidth. Hadoop framework handles data distributed over cluster.Main concentrationof this paper is on the usage of memory space or disk space. Experimentation is carried out through map reduce programs on different versions of Hadoop by changing some of the parameter values in the configuration files.

An independent time optimized hybrid infrastructure for big data analytics

In Big data domain, platform dependency can alter the behavior of the business. It is because of the different kinds (Structured, Semi-structured and Unstructured) and characteristics of the data. By the traditional infrastructure, different kinds of data cannot be processed simultaneously due to their platform dependency for a particular task. Therefore, the responsibility of selecting suitable tools lies with the user. The variety of data generated by different sources requires the selection of suitable tools without human intervention. Further, these tools also face the limitation of recourses to deal with a large volume of data. This limitation of resources affects the performance of the tools in terms of execution time. Therefore, in this work, we proposed a model in which different data analytics tools share a common infrastructure to provide data independence and resource sharing environment, i.e. the proposed model shares common (Hybrid) Hadoop Distributed File System (HDFS) between three Name-Node (Master Node), three Data-Node and one Client-node, which works under the DeMilitarized zone (DMZ). To realize this model, we have implemented Mahout, R-Hadoop and Splunk sharing a common HDFS. Further using our model, we run [Formula: see text]-means clustering, Naïve Bayes and recommender algorithms on three different datasets, movie rating, newsgroup, and Spam SMS dataset, representing structured, semi-structured and unstructured, respectively. Our model selected the appropriate tool, e.g. Mahout to run on the newsgroup dataset as other tools cannot run on this data. This shows that our model provides data independence. Further results of our proposed model are compared with the legacy (individual) model in terms of execution time and scalability. The improved performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model.

The psychometric properties of the Hungarian version of the Personality Inventory for DSM-5 in a clinical and a community sample

The alternative dimensional model for personality disorders characterizes the individual on pathological personality traits, which can be measured by the Personality Inventory for DSM-5 (PID-5). Our study focused on the psychometric properties of the Hungarian version of PID-5 in 239 psychiatric patients and 226 non-clinical participants. The distribution of gender, age, and educational level were the same in the two samples. In the clinical sample, we examined the assumed unidimensionality of the 25 facets. The results of the exploratory factor analysis for five factors indicated the supposed five-factor structure. The convergent validity was studied by correlations with Big Five Inventory. The PID-5 domains showed moderate or strong correlation with the domains of Big Five except for Psychoticism. The comparison of the clinical and non-clinical samples revealed that psychiatric patients had a higher score on all pathological facets and domains, except for the domain of Antagonism with all its related facets, and the facet of Risk Taking. As a whole, we recommend the use of the Hungarian version of the PID-5 in research and clinical practice.

Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning

Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so that the latter can be recognised without any training samples. This is made possible by learning a projection function between a feature space and a semantic space (e.g. attribute space). Considering the seen and unseen classes as two domains, a big domain gap often exists which challenges ZSL. Inspired by the fact that an unseen class is not exactly `unseen' if it belongs to the same superclass as a seen class, we propose a novel inductive ZSL model that leverages superclasses as the bridge between seen and unseen classes to narrow the domain gap. Specifically, we first build a class hierarchy of multiple superclass layers and a single class layer, where the superclasses are automatically generated by data-driven clustering over the semantic representations of all seen and unseen class names. We then exploit the superclasses from the class hierarchy to tackle the domain gap challenge in two aspects: deep feature learning and projection function learning. First, to narrow the domain gap in the feature space, we integrate a recurrent neural network (RNN) defined with the superclasses into a convolutional neural network (CNN), in order to enforce the superclass hierarchy. Second, to further learn a transferrable projection function for ZSL, a novel projection function learning method is proposed by exploiting the superclasses to align the two domains. Importantly, our transferrable feature and projection learning methods can be easily extended to a closely related task -- few-shot learning (FSL). Extensive experiments show that the proposed model significantly outperforms the state-of-the-art alternatives in both ZSL and FSL tasks.

Solving city and building microclimates by fast fluid dynamics with large timesteps and coarse meshes

Many urban and building microclimate problems are featured with a big computational domain and a large number of computing grids, so a fast and accurate solution by computation fluid dynamics (CFD) remains a challenge. The simulations are often only steady-state and, if there exists a transient model, the solution is often limited for a short period. For these computationally expensive simulations, large timesteps and coarse meshes (LTCM) seem preferable among users with limited computing resources, whereas many existing CFD models may not perform well for LTCM problems. This study developed a new fast fluid dynamics (FFD) model based on the semi-Lagrangian approach with high-order temporal and spatial schemes designed for LTCM problems. For large timesteps, a 2nd-order temporal scheme estimates the characteristic curve of each fluid's particle by accounting for the acceleration of the flow field. For coarse meshes, a 4th-order spatial scheme applies a backward-forward sweep technique for better accuracy in space. The performance of the proposed method is verified and validated by five benchmark cases, including a pure advection problem, forced convection in a single room, transient wind flow around a cylinder, airflow around a group of low-rise buildings, and airflow around a high-rise building. Then, we demonstrated the model for the study of the urban wind flows in a city downtown area with 2500 buildings and 35 million grids. As one of the first studies of applying FFD to urban aerodynamics, this study shows its strong potential for solving big transient problems with large timesteps and coarse meshes.

Research trends on big data domain using text mining algorithms

Abstract Most of the theories have considered big data as an interesting subject in the information technology domain. Big data is a term for describing huge databases that traditional methods in data processing suffer from analyzing them. Recognizing and clustering emerging topics in this area will help researchers whose aim is to work on this interesting subject. Text mining and social network analysis algorithms are utilized for identifying the emerging trends for big data domain. In this study, at first, we gathered the whole papers that are relevant to big data domain and then the word co-occurrence network was created based on the extracted keywords. Then the best clusters were identified and the relationship between keywords was recognized by the association rules technique. In conclusion, some suggestions were mentioned for future studies.

Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem

The class imbalance problem has been a hot topic in the machine learning community in recent years. Nowadays, in the time of big data and deep learning, this problem remains in force. Much work has been performed to deal to the class imbalance problem, the random sampling methods (over and under sampling) being the most widely employed approaches. Moreover, sophisticated sampling methods have been developed, including the Synthetic Minority Over-sampling Technique (SMOTE), and also they have been combined with cleaning techniques such as Editing Nearest Neighbor or Tomek’s Links (SMOTE+ENN and SMOTE+TL, respectively). In the big data context, it is noticeable that the class imbalance problem has been addressed by adaptation of traditional techniques, relatively ignoring intelligent approaches. Thus, the capabilities and possibilities of heuristic sampling methods on deep learning neural networks in big data domain are analyzed in this work, and the cleaning strategies are particularly analyzed. This study is developed on big data, multi-class imbalanced datasets obtained from hyper-spectral remote sensing images. The effectiveness of a hybrid approach on these datasets is analyzed, in which the dataset is cleaned by SMOTE followed by the training of an Artificial Neural Network (ANN) with those data, while the neural network output noise is processed with ENN to eliminate output noise; after that, the ANN is trained again with the resultant dataset. Obtained results suggest that best classification outcome is achieved when the cleaning strategies are applied on an ANN output instead of input feature space only. Consequently, the need to consider the classifier’s nature when the classical class imbalance approaches are adapted in deep learning and big data scenarios is clear.

The Extracellular Juncture Domains in the Intimin Passenger Adopt a Constitutively Extended Conformation Inducing Restraints to Its Sphere of Action

Enterohemorrhagic and enteropathogenic Escherichia coli are among the most important food-borne pathogens, posing a global health threat. The virulence factor intimin is essential for attachment of pathogenic E. coli to the intestinal host cell. Intimin consists of four extracellular bacterial immunoglobulin-like (Big) domains, D00-D2, extending into the fifth lectin subdomain (D3) that binds to the Tir-receptor on the host cell. Here, we present the crystal structures of the elusive D00-D0 domains at 1.5 A and D0-D1 at 1.8 A resolution that confirm that the passenger of intimin has five distinct domains. We describe that D00-D0 exhibits a higher degree of rigidity and D00 likely functions as a juncture domain at the outer membrane-extracellular medium interface. We conclude that D00 is a unique Big domain with a specific topology likely found in a broad range of other inverse autotransporters. The accumulated data allows us to model the complete passenger of intimin and propose functionality to the Big domains, D00-D0-D1, extending directly from the membrane.

Back to basics with receptors and ligands – molecules, mechanisms and medicine

Drug discovery is a complex, substantially long, technology-driven, labour-intensive and inordinately expensive process involving target discovery and validation, lead identification by high-throughput screening, and lead optimization by medicinal chemistry, preclinical evaluation in animal models, pharmacological screening (ADME screen: administration, distribution, metabolism, elimination) and studies of toxicology, specificity, and drug interactions. While comprehensive drug discovery workflows are predominantly undertaken in the big pharma domain, drug discovery in academia must not be discounted. Research relevant to drug discovery in academia is generally at the fundamental level, with a focus on drug mechanisms and the identification and validation of potential therapeutic targets, often in commercially unattractive, but relevant therapeutic areas particularly for the developing world, including rare disorders, parasitic diseases, and in research into natural products. Investigating natural sources for drug leads include compounds from venomous animals; with illustrious examples of success such as captopril (angiotensin converting enzyme inhibitor for the treatment of hypertension), exenatide (a glucagonlike peptide-1 receptor agonist for the treatment of type 2 diabetes) and ziconotide (an N-type calcium channel blocker for intractable pain)

Utilization of LabView in MIMO control for multiple tanks Level Measurement System

The modernisation of the existing device of facts acquisition machine in procedure manage is upgraded to the recent technologies for running the technique station. This paper offers the perspectives on the prevailing 3 tank structures installation is used for controlling the levels of two interacting systems. Liquid degree control has a completely big software domain in enterprise circulate over; the 3 tank system is one of the most broadly used experiments in laboratory & studies institutes. The measurement of business technique level parameter is one of the wonderful importances in manner manage. the extent of liquids may additionally have an effect on each the stress and fee of float in and out of the tank. subsequently, the excellent can be affected. automated manage applications would require control indicators for operation of actuation. stage transmitter is used to measure level of the tank. in this gadget the level in the process tank is sensed the usage of a degree transmitter and the corresponding present day output being measured and managed with the aid of the use of statistics acquisition gadget. [1],[ 3],[5]

From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification

The quality of the data is directly related to the quality of the models drawn from that data. For that reason, many research is devoted to improve the quality of the data and to amend errors that it may contain. One of the most common problems is the presence of noise in classification tasks, where noise refers to the incorrect labeling of training instances. This problem is very disruptive, as it changes the decision boundaries of the problem. Big Data problems pose a new challenge in terms of quality data due to the massive and unsupervised accumulation of data. This Big Data scenario also brings new problems to classic data preprocessing algorithms, as they are not prepared for working with such amounts of data, and these algorithms are key to move from Big to Smart Data. In this paper, an iterative ensemble filter for removing noisy instances in Big Data scenarios is proposed. Experiments carried out in six Big Data datasets have shown that our noise filter outperforms the current state-of-the-art noise filter in Big Data domains. It has also proved to be an effective solution for transforming raw Big Data into Smart Data.

Association between second to fourth digit ratio and personality among Indian men.

To examine the relationship between the personality domains with the second to fourth digit ratio (2d:4d); a prenatal testosterone exposure marker. The 2d:4d for both hands was measured for the Indian male subjects' (n = 994). Ten-item personality inventory and displaced aggression questionnaire data were collected in a self-reported proforma in this cross sectional study with a convenient sampling approach. The personality traits, namely openness to new experiences, carelessness, and self-discipline were significantly differed between lower-half and higher-half of 2d:4d by median split. The right hand 2d:4d was positively correlated with open to new experiences and negatively correlated with disorganization, carelessness, and revenge planning. The left hand 2d:4d was positively correlated with aggression. On binary logistic regression, openness was positively associated while carelessness was negatively associated with the right hand 2d:4d and self-discipline trait was negatively associated with the left hand 2d:4d. This study affirmed the link between 2d:4d and domains of Big Five personality factors among Indian men and inverse relationship between 2d:4d and more "female" hands in the domains of disorganization, carelessness, and revenge planning even in men, emphasizing the effect of prenatal testosterone exposure on human personality.

Evaluation of maxout activations in deep learning across several big data domains

This study investigates the effectiveness of multiple maxout activation function variants on 18 datasets using Convolutional Neural Networks. A network with maxout activation has a higher number of trainable parameters compared to networks with traditional activation functions. However, it is not clear if the activation function itself or the increase in the number of trainable parameters is responsible in yielding the best performance for different entity recognition tasks. This paper investigates if an increase in the number of convolutional filters on traditional activation functions performs equal-to or better-than maxout networks. Our experiments compare the Rectified Linear Unit, Leaky Rectified Linear Unit, Scaled Exponential Linear Unit, and Hyperbolic Tangent activations to four maxout function variants. We observe that maxout networks train relatively slower than networks with traditional activation functions, e.g. Rectified Linear Unit. In addition, we found that on average, across all datasets, the Rectified Linear Unit activation function performs better than any maxout activation when the number of convolutional filters is increased. Furthermore, adding more filters enhances the classification accuracy of the Rectified Linear Unit networks, without adversely affecting their advantage over maxout activations with respect to network-training speed.

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry

Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems. Databases PubMed and PsycINFO were searched from 1966 to June 2016 for keywords:Big Data, Machine Learning, Precision Medicine, Artificial Intelligence, Mental Health, Mental Disease, Psychiatry, Data Mining, RDoC, and Research Domain Criteria. Articles selected for review were those that were determined to be aligned with the objective of this particular paper. Results indicate that AI is a viable option to build useful predictors of outcome while offering objective and comparable accuracy metrics, a unique opportunity, particularly in mental health research. The approach has also consistently brought notable insight into disease models through processing the vast amount of already available multi-domain, semi-structured medical data. The opportunity for AI in psychiatry, in addition to disease-model refinement, is in characterizing those at risk, and it is likely also relevant to personalizing and discovering therapeutics. Machine learning currently provides an opportunity to parse disease models in complex, multi-factorial disease states (e.g. mental disorders) and could possibly inform treatment selection with existing therapies and provide bases for domain-based therapeutic discovery.

Semantic Earth Observation Data Cubes

There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data.