Challenges of Big Data Analysis

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article give overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasis on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions

Large-scale Data Analysis and Deep Learning Using Distributed Cyberinfrastructures and High Performance Computing

Data in many research fields continues to grow in both size and complexity. For instance, recent technological advances have caused an increased throughput in data in various biological-related endeavors, such as DNA sequencing, molecular simulations, and medical imaging. In addition, the variance in the types of data (textual, signal, image, etc.) adds an additional complexity in analyzing the data. As such, there is a need for uniquely developed applications that cater towards the type of data. Several considerations must be made when attempting to create a tool for a particular dataset. First, we must consider the type of algorithm required for analyzing the data. Next, since the size and complexity of the data imposes high computation and memory requirements, it is important to select a proper hardware environment on which to build the application. By carefully both developing the algorithm and selecting the hardware, we can provide an effective environment in which to analyze huge amounts of highly complex data in a large-scale manner. In this dissertation, I go into detail regarding my applications using big data and deep learning techniques to analyze complex and large data. I investigate how big data frameworks, such as Hadoop, can be applied to problems such as large-scale molecular dynamics simulations. Following this, many popular deep learning frameworks are evaluated and compared to find those that suit certain hardware setups and deep learning models. Then, we explore an application of deep learning to a biomedical problem, namely ADHD diagnosis from fMRI data. Lastly, I demonstrate a framework for real-time and fine-grained vehicle detection and classification. With each of these works in this dissertation, a unique large-scale analysis algorithm or deep learning model is implemented that caters towards the problem and leverages specialized computing resources

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Big data analytics: Machine learning and Bayesian learning perspectives—What is done? What is not?

Big data analytics provides an interdisciplinary framework that is essential to support the current trend for solving real-world problems collaboratively. The progression of big data analytics framework must be clearly understood so that novel approaches can be developed to advance this state-of-the-art discipline. An ignorance of observing the progression of this fast-growing discipline may lead to duplications in research and waste of efforts. Its main companion field, machine learning, helps solve many big data analytics problems; therefore, it is also important to understand the progression of machine learning in the big data analytics framework. One of the current research efforts in big data analytics is the integration of deep learning and Bayesian optimization, which can help the automatic initialization and optimization of hyperparameters of deep learning and enhance the implementation of iterative algorithms in software. The hyperparameters include the weights used in deep learning, and the number of clusters in Bayesian mixture models that characterize data heterogeneity. The big data analytics research also requires computer systems and software that are capable of storing, retrieving, processing, and analyzing big data that are generally large, complex, heterogeneous, unstructured, unpredictable, and exposed to scalability problems. Therefore, it is appropriate to introduce a new research topic—transformative knowledge discovery—that provides a research ground to study and develop smart machine learning models and algorithms that are automatic, adaptive, and cognitive to address big data analytics problems and challenges. The new research domain will also create research opportunities to work on this interdisciplinary research space and develop solutions to support research in other disciplines that may not have expertise in the research area of big data analytics. For example, the research, such as detection and characterization of retinal diseases in medical sciences and the classification of highly interacting species in environmental sciences can benefit from the knowledge and expertise in big data analytics