Computer Engineering Commons^™

Feature Space Modeling For Accurate And Efficient Learning From Non-Stationary Data, Ayesha Akter

Doctoral Dissertations

A non-stationary dataset is one whose statistical properties such as the mean, variance, correlation, probability distribution, etc. change over a specific interval of time. On the contrary, a stationary dataset is one whose statistical properties remain constant over time. Apart from the volatile statistical properties, non-stationary data poses other challenges such as time and memory management due to the limitation of computational resources mostly caused by the recent advancements in data collection technologies which generate a variety of data at an alarming pace and volume. Additionally, when the collected data is complex, managing data complexity, emerging from its dimensionality and …

Using Power-Law Properties Of Social Groups For Cloud Defense And Community Detection, Justin L. Rice

Doctoral Dissertations

The power-law distribution can be used to describe various aspects of social group behavior. For mussels, sociobiological research has shown that the Lévy walk best describes their self-organizing movement strategy. A mussel's step length is drawn from a power-law distribution, and its direction is drawn from a uniform distribution. In the area of social networks, theories such as preferential attachment seek to explain why the degree distribution tends to be scale-free. The aim of this dissertation is to glean insight from these works to help solve problems in two domains: cloud computing systems and community detection.

Privacy and security are …

Reliability Models For Hpc Applications And A Cloud Economic Model, Thanadech Thanakornworakij

Doctoral Dissertations

With the enormous number of computing resources in HPC and Cloud systems, failures become a major concern. Therefore, failure behaviors such as reliability, failure rate, and mean time to failure need to be understood to manage such a large system efficiently.

This dissertation makes three major contributions in HPC and Cloud studies. First, a reliability model with correlated failures in a k-node system for HPC applications is studied. This model is extended to improve accuracy by accounting for failure correlation. Marshall-Olkin Multivariate Weibull distribution is improved by excess life, conditional Weibull, to better estimate system reliability. Also, the univariate …

Data Mining Based Learning Algorithms For Semi-Supervised Object Identification And Tracking, Michael P. Dessauer

Doctoral Dissertations

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify, …