A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Big Data Analytics for Complex Systems

The evolution of technology in all fields led to the generation of vast amounts of data by modern systems. Using data to extract information, make predictions, and make decisions is the current trend in artificial intelligence. The advancement of big data analytics tools made accessing and storing data easier and faster than ever, and machine learning algorithms help to identify patterns in and extract information from data. The current tools and machines in health, computer technologies, and manufacturing can generate massive raw data about their products or samples. The author of this work proposes a modern integrative system that can utilize big data analytics, machine learning, super-computer resources, and industrial health machines’ measurements to build a smart system that can mimic the human intelligence skills of observations, detection, prediction, and decision-making. The applications of the proposed smart systems are included as case studies to highlight the contributions of each system. The first contribution is the ability to utilize big data revolutionary and deep learning technologies on production lines to diagnose incidents and take proper action. In the current digital transformational industrial era, Industry 4.0 has been receiving researcher attention because it can be used to automate production-line decisions. Reconfigurable manufacturing systems (RMS) have been widely used to reduce the setup cost of restructuring production lines. However, the current RMS modules are not linked to the cloud for online decision-making to take the proper decision; these modules must connect to an online server (super-computer) that has big data analytics and machine learning capabilities. The online means that data is centralized on cloud (supercomputer) and accessible in real-time. In this study, deep neural networks are utilized to detect the decisive features of a product and build a prediction model in which the iFactory will make the necessary decision for the defective products. The Spark ecosystem is used to manage the access, processing, and storing of the big data streaming. This contribution is implemented as a closed cycle, which for the best of our knowledge, no one in the literature has introduced big data analysis using deep learning on real-time applications in the manufacturing system. The code shows a high accuracy of 97% for classifying the normal versus defective items. The second contribution, which is in Bioinformatics, is the ability to build supervised machine learning approaches based on the gene expression of patients to predict proper treatment for breast cancer. In the trial, to personalize treatment, the machine learns the genes that are active in the patient cohort with a five-year survival period. The initial condition here is that each group must only undergo one specific treatment. After learning about each group (or class), the machine can personalize the treatment of a new patient by diagnosing the patients’ gene expression. The proposed model will help in the diagnosis and treatment of the patient. The future work in this area involves building a protein-protein interaction network with the selected genes for each treatment to first analyze the motives of the genes and target them with the proper drug molecules. In the learning phase, a couple of feature-selection techniques and supervised standard classifiers are used to build the prediction model. Most of the nodes show a high-performance measurement where accuracy, sensitivity, specificity, and F-measure ranges around 100%. The third contribution is the ability to build semi-supervised learning for the breast cancer survival treatment that advances the second contribution. By understanding the relations between the classes, we can design the machine learning phase based on the similarities between classes. In the proposed research, the researcher used the Euclidean matrix distance among each survival treatment class to build the hierarchical learning model. The distance information that is learned through a non-supervised approach can help the prediction model to select the classes that are away from each other to maximize the distance between classes and gain wider class groups. The performance measurement of this approach shows a slight improvement from the second model. However, this model reduced the number of discriminative genes from 47 to 37. The model in the second contribution studies each class individually while this model focuses on the relationships between the classes and uses this information in the learning phase. Hierarchical clustering is completed to draw the borders between groups of classes before building the classification models. Several distance measurements are tested to identify the best linkages between classes. Most of the nodes show a high-performance measurement where accuracy, sensitivity, specificity, and F-measure ranges from 90% to 100%. All the case study models showed high-performance measurements in the prediction phase. These modern models can be replicated for different problems within different domains. The comprehensive models of the newer technologies are reconfigurable and modular; any newer learning phase can be plugged-in at both ends of the learning phase. Therefore, the output of the system can be an input for another learning system, and a newer feature can be added to the input to be considered for the learning phase

Big Data Now, 2015 Edition

Now in its fifth year, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve talked about over the past year. For 2015, we’ve included a collection of blog posts, authored by leading thinkers and experts in the field, that reflect a unique set of themes we’ve identified as gaining significant attention and traction. Our list of 2015 topics include: Data-driven cultures Data science Data pipelines Big data architecture and infrastructure The Internet of Things and real time Applications of big data Security, ethics, and governance Is your organization on the right track? Get a hold of this free report now and stay in tune with the latest significant developments in big data

Integrative methods for analysing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

Modeling Approaches for Cost and Cost-Effectiveness Estimation Using Observational Data

The estimation of treatment effects on medical costs and cost effectiveness measures is complicated by the need to account for non-independent censoring, skewness and the effects of confounders. In this dissertation, we develop several cost and cost-effectiveness tools that account for these issues. Since medical costs are often collected from observational claims data, we investigate propensity score methods such as covariate adjustment, stratification, inverse probability weighting and doubly robust weighting. We also propose several doubly robust estimators for common cost effectiveness measures. Lastly, we explore the role of big data tools and machine learning algorithms in cost estimation. We show how these modern techniques can be applied to big data manipulation, cost prediction and dimension reduction