Physical Sciences and Mathematics Commons^™

Automated Negotiation Among Web Services, Khayyam Hashmi

Wayne State University Dissertations

Software as a service is well accepted software deployment and distribution model that is grown exponentially in the last few years. One of the biggest benefits of SaaS is the automated composition of these services in a composite system. It allows users to automatically find and bind these services, as to maximize the productivity of their composed systems, meeting both functional and non-functional requirements. In this paper we present a framework for modeling the dependency relationship of different Quality of Service parameters of a component service. Our proposed approach considers the different invocation patterns of component services in the system …

Improving User Experience In Information Retrieval Using Semantic Web And Other Technologies, Erfan Najmi

Wayne State University Dissertations

The need to find, access and extract information has been the motivation for many

different fields of research in the past few years. The fields such as Machine Learning,

Question Answering Systems, Semantic Web, etc. each tries to cover parts of the

mentioned problem. Each of these fields have introduced many different tools and

approaches which in many cases are multi-disciplinary, covering more than one of

these fields to provide solution for one or more of them. On the other hand, the

expansion of the Web with Web 2.0, gave researchers many new tools to extend

approaches to help users …

Feature Grouping Using Weighted L1 Norm For High-Dimensional Data, Karthik Kumar Padthe`

Wayne State University Theses

Building effective prediction models from high-dimensional data is an important problem in several domains such as in bioinformatics, healthcare analytics and general regression analysis. Extracting feature groups automatically from such data with several correlated features is necessary, in order to use regularizers such as the group lasso which can exploit this deciphered grouping structure to build effective prediction models. Elastic net, fused-lasso and Octagonal Shrinkage Clustering Algorithm for Regression (oscar) are some of the popular feature grouping methods proposed in the literature which recover both sparsity and feature groups from the data. However, their predictive ability is affected adversely when …

System Support For Energy Efficient Mobile Computing, Youhuizi Li

Wayne State University Dissertations

Mobile devices are developed rapidly and they have been an integrated part of our daily life. With the blooming of Internet of Things, mobile computing will become more and more important. However, the battery drain problem is a critical issue that hurts user experience. High performance devices require more power support, while the battery capacity only increases 5% per year on average. Researchers are working on kinds of energy saving approaches. For examples, hardware components provide different power state to save idle power; operating systems provide power management APIs to better control power dissipation. However, the system energy efficiency is …

Sharing-Aware Resource Management Algorithms For Virtual Computing Environments, Safraz Rampersaud

Wayne State University Dissertations

Virtualization technologies in cloud computing are ubiquitous throughout data centers around the world where providers consider operational costs and fast delivery guarantees for a variety of profitable services. These providers should consistently invoke measures for increasing the efficiencies of their virtualized services in a competitive environment where fast entry to market, technology advancement, and service price differentials separate sustaining providers from antiquated ones. Therefore, providers seeking further efficiencies and profit opportunities should consider how their resources are managed in virtual computing environments which leverage memory reclamation techniques, specifically page-sharing; motivating the design of new memory sharing-aware resource management algorithms. In …

Big Data Management Using Scientific Workflows, Andrii Kashliev

Wayne State University Dissertations

Humanity is rapidly approaching a new era, where every sphere of activity will be informed by the ever-increasing amount of data. Making use of big data has the potential to improve numerous avenues of human activity, including scientific research, healthcare, energy, education, transportation, environmental science, and urban planning, just to name a few. However, making such progress requires managing terabytes and even petabytes of data, generated by billions of devices, products, and events, often in real time, in different protocols, formats and types. The volume, velocity, and variety of big data, known as the "3 Vs", present formidable challenges, unmet …

Novel Regression Models For High-Dimensional Survival Analysis, Yan Li

Wayne State University Dissertations

Survival analysis aims to predict the occurrence of specific events of interest at future time points. The presence of incomplete observations due to censoring brings unique challenges in this domain and differentiates survival analysis techniques from other standard regression methods. In this thesis, we propose four models to deal with the high-dimensional survival analysis. Firstly, we propose a regularized linear regression model with weighted least-squares to handle the survival prediction in the presence of censored instances. We employ the elastic net penalty term for inducing sparsity into the linear model to effectively handle high-dimensional data. As opposed to the existing …

User-Centric Power Management For Mobile Operating Systems, Hui Chen

Wayne State University Dissertations

The power consumption of mobile devices must be carefully managed to provide a satisfied battery life to users. This target, however, recently has become more and more difficult to complete. We still cannot expect the battery life problem be solved economically shortly, even though researchers already addressed many aspects of this problem. Principally, that's because existing power management systems, which concentrate on controlling hardware power states, cannot effectively make these hardware components work in low-power mode. Why is this the case?

Based on our analysis of 14 users' device usage trace, we found that background applications generate too many activities …

Consensus Regularized Selection Based Prediction, Ping Wang

Wayne State University Theses

Integrating regularization methods within a regression framework has become a popular choice for researchers to build predictive models with lower variance and better generalization. Regularizers also aid in building interpretable models with high-dimensional data which makes them very appealing. Regularizers in general are unique in nature as they cater to data specific features such as correlation, structured sparsity, and temporal smoothness. The problem of obtaining a consensus among such diverse regularizers is extremely important in order to determine the optimal regularizer for the model. This is called the consensus regularization problem which has not received much attention in the literature, …

Interactive Refinement Of Hierarchical Object Graphs, Ebrahim Khalaj

Wayne State University Theses

Developers need to understand the runtime structure of object-oriented code, and abstract object graphs can help. To extract abstract object graphs that convey design intent in the form of object hierarchy, additional information is needed to express this hierarchy in the code using ownership types, but adding ownership type qualifiers after the fact involves manual overhead, and requires developers to switch between adding qualifiers in the code and looking at abstract object graphs to understand the object structures that the qualifiers describe. We describe an approach where developers express their design intent by refining an object graph directly, while an …

Dynamic Privacy Management In Services Based Interactions, Nariman Tm Ammar

Wayne State University Dissertations

Technology advancements have enabled the distribution and sharing of users personal data over several data sources. Each data source is potentially managed by a different organization, which may expose its data as a Web service. Using such Web services, dynamic composition of atomic data items coupled with the context in which the data is accessed may breach sensitive data that may not comply with the users preference at the time of data collection. Thus, providing uniform access policies to such data can lead to privacy problems. Some fairly recent research has focused on providing solutions for dynamic privacy management. This …

Force Field Development With Gomc A Fast New Monte Carlo Molecular Simulation Code, Jason Richard Mick

Wayne State University Dissertations

In this work GOMC (GPU Optimized Monte Carlo) a new fast, flexible, and free molecular Monte Carlo code for the simulation atomistic chemical systems is presented. The results of a large Lennard-Jonesium simulation in the Gibbs ensemble is presented. Force fields developed using the code are also presented. To fit the models a quantitative fitting process is outlined using a scoring function and heat maps. The presented n-6 force fields include force fields for noble gases and branched alkanes. These force fields are shown to be the most accurate LJ or n-6 force fields to date for these compounds, capable …

The Relation Between Patient Education And Picc Line Occlusion, Patricia Louise Petroulias

Wayne State University Dissertations

The purpose of this study was to test the feasibility of using an electronic tablet to supplement patient and caregiver education about PICC line maintenance and to compare the percentage of PICC line complications in the intervention group with national percentage rates for patients and caregivers. Newly diagnosed cancer patients who had PICC lines inserted participated in this study. They used an electronic tablet to learn the 10 steps for flushing their PICC lines correctly. They also met with the researcher via FaceTime to review the steps.

A total of 11 patients participated in this quasi-experimental pilot study. They completed …

Novel Machine Learning Methods For Modeling Time-To-Event Data, Bhanukiran Vinzamuri

Wayne State University Dissertations

Predicting time-to-event from longitudinal data where different events occur at different time points is an extremely important problem in several domains such as healthcare, economics, social networks and seismology, to name a few. A unique challenge in this problem involves building predictive models from right censored data (also called as survival data). This is a phenomenon where instances whose event of interest are not yet observed within a given observation time window and are considered to be right censored. Effective models for predicting time-to-event labels from such right censored data with good accuracy can have a significant impact in these …