Computer Sciences Commons^™

An Integrated Transport Solution To Big Data Movement In High-Performance Networks, Daqing Yun

Dissertations

Extreme-scale e-Science applications in various domains such as earth science and high energy physics among multiple national institutions within the U.S. are generating colossal amounts of data, now frequently termed as “big data”. The big data must be stored, managed and moved to different geographical locations for distributed data processing and analysis. Such big data transfers require stable and high-speed network connections, which are not readily available in traditional shared IP networks such as the Internet. High-performance networking technologies and services featuring high bandwidth and advance reservation are being rapidly developed and deployed across the nation and around the globe …

Accelerating Data-Intensive Scientific Visualization And Computing Through Parallelization, Dongliang Chu

Dissertations

Many extreme-scale scientific applications generate colossal amounts of data that require an increasing number of processors for parallel processing. The research in this dissertation is focused on optimizing the performance of data-intensive parallel scientific visualization and computing.

In parallel scientific visualization, there exist three well-known parallel architectures, i.e., sort-first/middle/last. The research in this dissertation studies the composition stage of the sort-last architecture for scientific visualization and proposes a generalized method, namely, Grouping More and Pairing Less (GMPL), for order-independent image composition workflow scheduling in sort-last parallel rendering. The technical merits of GMPL are two-fold: i) it takes a prime factorization-based …

Towards Trustworthy Version Control Systems: Enhancing The Security Of Subversion, Ruchir Arya

Theses

Software development often relies on a Version Control System (VCS) to manage the source code, documentation and configuration of files. A VCS allows team development in which multiple developers can work simultaneously on source code updates. It also provides the ability to keep track of the historical changes made to the data over time, including the ability to retrieve previous versions of the source code in order to locate and fix bugs, and roll back to earlier versions in case the working version becomes buggy or unstable.

Apache Subversion (SVN) is a popular version control system that uses a client-server …

Context-Aware Collaborative Storage And Programming For Mobile Users, Mohammad A. Khan

Dissertations

Since people generate and access most digital content from mobile devices, novel innovative mobile apps and services are possible. Most people are interested in sharing this content with communities defined by friendship, similar interests, or geography in exchange for valuable services from these innovative apps. At the same time, they want to own and control their content. Collaborative mobile computing is an ideal choice for this situation. However, due to the distributed nature of this computing environment and the limited resources on mobile devices, maintaining content availability and storage fairness as well as providing efficient programming frameworks are challenging.

This …

Termination, Correctness And Relative Correctness, Nafi Diallo

Dissertations

Over the last decade, research in verification and formal methods has been the subject of increased interest with the need of more secure and dependable software. At the heart of software dependability is the concept of software fault, defined in the literature as the adjudged or hypothesized cause of an error. This definition, which lacks precision, presents at least two challenges with regard to using formal methods: (1) Adjudging and hypothesizing are highly subjective human endeavors; (2) The concept of error is itself insufficiently defined, since it depends on a detailed characterization of correct system states at each stage of …

A Data Science Approach To Pattern Discovery In Complex Structures With Applications In Bioinformatics, Lei Hua

Dissertations

Pattern discovery aims to find interesting, non-trivial, implicit, previously unknown and potentially useful patterns in data. This dissertation presents a data science approach for discovering patterns or motifs from complex structures, particularly complex RNA structures. RNA secondary and tertiary structure motifs are very important in biological molecules, which play multiple vital roles in cells. A lot of work has been done on RNA motif annotation. However, pattern discovery in RNA structure is less studied. In the first part of this dissertation, an ab initio algorithm, named DiscoverR, is introduced for pattern discovery in RNA secondary structures. This algorithm works by …

Schema-Aware Keyword Search On Linked Data, Ananya Dass

Dissertations

Keyword search is a popular technique for querying the ever growing repositories of RDF graph data on the Web. This is due to the fact that the users do not need to master complex query languages (e.g., SQL, SPARQL) and they do not need to know the underlying structure of the data on the Web to compose their queries. Keyword search is simple and flexible. However, it is at the same time ambiguous since a keyword query can be interpreted in different ways. This feature of keyword search poses at least two challenges: (a) identifying relevant results among a multitude …

Collaborative Development Of A Small Business Emergency Planning Model, Arthur Henry Hendela

Dissertations

Small businesses, which are defined by the US Small Business Administration as entities with less than 500 employees, suffer interruptions from diverse risks such as financial events, legal situations, or severe storms exemplified by Hurricane Sandy. Proper preparations can help lessen the length of the interruption and put employees and owners back to work. Large corporations generally have large budgets available for planning, business continuity, and disaster recovery. Small businesses must decide which risks are the most important and how best to mitigate those risks using minimal resources.

This research uses a series of surveys followed by mathematical modeling to …

Mediating Chance Encounters Through Opportunistic Social Matching, Julia M. Mayer

Dissertations

Chance encounters, the unintended meeting between people unfamiliar with each other, serve as an important social lubricant helping people to create new social ties, such as making new friends or finding an activity, study or collaboration partner. Unfortunately, social barriers often prevent chance encounters in environments where people do not know each other and people have to rely on serendipity to meet or be introduced to interesting people around them. Little is known about the underlying dynamics of chance encounters and how systems could utilize contextual data to mediate chance encounters. This dissertation addresses this gap in research literature by …

Semi Supervised Weighted Maximum Variance Dimensionality Reduction, Pranitha Surya Andalam

Theses

In the recent years, we have huge amounts of data which we want to classify with minimal human intervention. Only few features from the data that is available might be useful in some scenarios. In those scenarios, the dimensionality reduction methods play a major role for extracting useful features. The two parameter weighted maximum variance (2P-WMV) is a generalized dimensionality reduction method of which principal component analysis (PCA) and maximum margin criterion (MMC) are special cases.. In this paper, we have extended the 2P-WMV approach from our previous work to a semi-supervised version. The objective of this work is specially …

Data Analytics With Mapreduce In Apache Spark And Hadoop Systems, Zongxuan Du

Theses

MapReduce comes from a traditional problem solving method: separating a big problem and solving each small parts. With the target of computing larger dataset in more efficient and cheaper way, this is implement into a programming mode to deal with massive quantity of data. The users get a map function and use it to abstract dataset into key / value logical pair and then use a reduce function to group all value with the same key. With this mode, task can be automatic spread the job into clusters grouped by lots of normal computers. MapReduce program can be easily implemented …

Gene Network Understanding And Analysis, Maria E. Somoza

Theses

Gene regulatory network (GRN) is a collection of regulators that interact with each other in the cell to govern the gene expression levels of mRNA and proteins. These regulators can either be DNA, RNA, protein and their complex. Transcriptional gene regulation is an important mechanisms in which an in-depth study can lead to various practical applications, and a greater understanding of how organisms control their cellular behavior. One of the most widely studied organisms in gene regulatory networks are the Mycobacterium tuberculosis and Corynebacterium glutamicum ATCC 13032.

Gene co-expression networks are of biological interests due to co-expressed genes which are …

Uusing The Kdj As A Trading Strategy On Biotech Companies, Shijie Zha

Theses

Mean Reversion is the most commonly used model in quantitative trading. This model is associated with several factors, like ma5 and ma10 line. These factors are the most significant in stock markets. However, the disadvantages of this model are lag and inaccuracy.

In this research, we get the historical and current stock data by web crawler, analyze the quantitative data and build a new model involved with the KDJ. Taking biotech companies marketed in the United States and B-share marketed in China as the research subjects, the result shows increased profits compared with the Mean Reversion model. It also shows …

Semantics And Result Disambiguation For Keyword Search On Tree Data, Cem Aksoy

Dissertations

Keyword search is a popular technique for searching tree-structured data (e.g., XML, JSON) on the web because it frees the user from learning a complex query language and the structure of the data sources. However, the convenience of keyword search comes with drawbacks. The imprecision of the keyword queries usually results in a very large number of results of which only very few are relevant to the query. Multiple previous approaches have tried to address this problem. Some of them exploit structural and semantic properties of the tree data in order to filter out irrelevant results while others use a …

Task-Based User Profiling For Query Refinement (Toque), Chao Xu

Dissertations

The information needs of search engine users vary in complexity. Some simple needs can be satisfied by using a single query, while complicated ones require a series of queries spanning a period of time. A search task, consisting of a sequence of search queries serving the same information need, can be treated as an atomic unit for modeling user’s search preferences and has been applied in improving the accuracy of search results. However, existing studies on user search tasks mainly focus on applying user’s interests in re-ranking search results. Only few studies have examined the effects of utilizing search tasks …

Social Analytics For Health Integration, Intelligence, And Monitoring, Xiang Ji

Dissertations

Nowadays, patient-generated social health data are abundant and Healthcare is changing from the authoritative provider-centric model to collaborative and patient-oriented care. The aim of this dissertation is to provide a Social Health Analytics framework to utilize social data to solve the interdisciplinary research challenges of Big Data Science and Health Informatics. Specific research issues and objectives are described below.

The first objective is semantic integration of heterogeneous health data sources, which can vary from structured to unstructured and include patient-generated social data as well as authoritative data. An information seeker has to spend time selecting information from many websites and …

Continuous Monitoring Of Enterprise Risks: A Delphi Feasibility Study, Robert Baksa

Dissertations

A constantly evolving regulatory environment, increasing market pressure to improve operations, and rapidly changing business conditions are creating the need for ongoing assurance that organizational risks are continually and adequately mitigated. Enterprises are perpetually exposed to fraud, poor decision making and/or other inefficiencies that can lead to significant financial loss and/or increased levels of operating risk. Increasingly, Information Systems are being harnessed to reinvent the risk management process. One promising technology is Continuous Auditing, which seeks to transform the audit process from periodic reviews of a few transactions to a continuous review of all transactions. However, the highly integrated, rapidly …

Data Mining In Computational Proteomics And Genomics, Yang Song

Dissertations

This dissertation addresses data mining in bioinformatics by investigating two important problems, namely peak detection and structure matching. Peak detection is useful for biological pattern discovery while structure matching finds many applications in clustering and classification.

The first part of this dissertation focuses on elastic peak detection in 2D liquid chromatographic mass spectrometry (LC-MS) data used in proteomics research. These data can be modeled as a time series, in which the X-axis represents time points and the Y-axis represents intensity values. A peak occurs in a set of 2D LC-MS data when the sum of the intensity values in a …

Designing Novel Abstraction Networks For Ontology Summarization And Quality Assurance, Christopher Ochs

Dissertations

Biomedical ontologies are complex knowledge representation systems. Biomedical ontologies support interdisciplinary research, interoperability of medical systems, and Electronic Healthcare Record (EHR) encoding. Ontologies represent knowledge using concepts (entities) linked by relationships. Ontologies may contain hundreds of thousands of concepts and millions of relationships. For users, the size and complexity of ontologies make it difficult to comprehend “the big picture” of an ontology's content. For ontology editors, size and complexity make it difficult to uncover errors and inconsistencies. Errors in an ontology will ultimately affect applications that utilize the ontology.

In prior studies abstraction networks (AbNs) were developed to provide a …

Information Filtering By Multiple Examples, Mingzhu Zhu

Dissertations

A key to successfully satisfy an information need lies in how users express it using keywords as queries. However, for many users, expressing their information needs using keywords is difficult, especially when the information need is complex. Search By Multiple Examples (SBME), a promising method for overcoming this problem, allows users to specify their information needs as a set of relevant documents rather than as a set of keywords.

Most of the studies on SBME adopt the Positive Unlabeled learning (PU learning) techniques by treating the user's provided examples (denoted as query examples) as positive set and the entire data …

Assessing Learning Outcomes And Social Capital Formation Resulting From The Use And Sharing Of Internet Knowledge Resources, Regina S. Collins

Dissertations

Today’s “digital natives” use the Internet to address most, if not all, their learning-related knowledge needs. This research evaluates the outcomes of formal learning activities requiring students to use, manage, share, and consolidate Internet knowledge resources (such as websites, videos, and blogs) to achieve both individual and group learning. This research takes an integrative approach to learning, capturing learner cognitive, interpersonal, and intrapersonal characteristics as well as the impact of the digital environment by evaluating the technological affordances of two different systems supporting such learning activities. This research also examines pedagogical modifications that would best integrate course assignments utilizing Internet …

Exact Genome Alignment, Nandini Ghosh

Theses

The increase in the volume of genomic data due to the decrease in the cost of whole genome sequencing techniques has opened up new avenues of research in the field of Bioinformatics, like comparative genomics and evolutionary dynamics. The fundamental task in these studies is to align the genome sequences accurately. Sequence alignment helps to identify regions of similarity between the sequences to establish their functional, evolutionary and structural relationship. The thesis investigates the performance of two sequence alignment programs LASTZ, a hash table based faster method and SSEARCH, a slower but more rigorous Smith-Waterman based approach, on whole genome …

Unsupervised Gene Regulatory Network Inference On Microarray Data, Nidhi Radia

Theses

Obtaining gene regulatory networks (GRNs) from expression data is a challenging and crucial task. Many computational methods and algorithms have been developed to infer gene networks for gene expression data, which are usually obtained from microarray experiments. A gene network is a method to depict the relation among clusters of genes. To infer gene networks, the unsupervised method is used in this study. The two types of data used are time-series data and steady-state data. The data is analyzed using various tools containing different algorithms and concepts. GRNs from time-series data tools are obtained using the Time-delayed Algorithm for the …

Identifying Modifier Genes In Sma Model Mice, Weiting Xu

Theses

Spinal Muscular Atrophy (SMA) involves the loss of nerve cells called motor neurons in the spinal cord and is classified as a motor neuron disease, it affects 1 in 5000-10000 newborns, one of the leading genetic causes of infant death in USA. Mutations in the SMN1, UBA1, DYNC1H1 and VAPB genes cause spinal muscular atrophy. Extra copies of the SMN2 gene modify the severity of spinal muscular atrophy. Mutations in SMN1 (Motor Neuron 1) mainly causes SMA (Autosomal recessive inheritance). SMN1 gene mutations lead to a shortage of the SMN protein and SMN protein forms SMN complex …

Design And Analysis Of Algorithms For Similarity Search Based On Intrinsic Dimension, Xiguo Ma

Dissertations

One of the most fundamental operations employed in data mining tasks such as classification, cluster analysis, and anomaly detection, is that of similarity search. It has been used in numerous fields of application such as multimedia, information retrieval, recommender systems and pattern recognition. Specifically, a similarity query aims to retrieve from the database the most similar objects to a query object, where the underlying similarity measure is usually expressed as a distance function.

The cost of processing similarity queries has been typically assessed in terms of the representational dimension of the data involved, that is, the number of features used …

Location Reliability And Gamification Mechanisms For Mobile Crowd Sensing, Manoop Talasila

Dissertations

People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the sensors on the phones. This new type of sensing can be a scalable and cost-effective alternative to deploying static wireless sensor networks for dense sensing coverage across large areas. However, mobile people-centric sensing has two main issues: 1) Data reliability in sensed data and 2) Incentives for participants. To study these issues, this dissertation designs and develops McSense, a mobile crowd sensing system which provides monetary and social incentives to users.

This dissertation proposes and evaluates two protocols for location reliability …

Adaptive Global Optimization Algorithms, William Phillips

Dissertations

Global optimization is concerned with finding the minimum value of a function where many local minima may exist. The development of a global optimization algorithm may involve using information about the target function (e.g., differentiability) and functions based on statistical models to better the worst case time complexity and expected error of similar deterministic algorithms.

Recent algorithms are investigated, new ones proposed and their performance is analyzed. Minimum, maximum and average case error bounds for the algorithms presented are derived. Software architecture implemented with MATLAB and Java is presented and experimental results for the algorithms are displayed.

The graphical capabilities …

Enabling Virtualization Technologies For Enhanced Cloud Computing, Kashifuddin Qazi

Dissertations

Cloud Computing is a ubiquitous technology that offers various services for individual users, small businesses, as well as large scale organizations. Data-center owners maintain clusters of thousands of machines and lease out resources like CPU, memory, network bandwidth, and storage to clients. For organizations, cloud computing provides the means to offload server infrastructure and obtain resources on demand, which reduces setup costs as well as maintenance overheads. For individuals, cloud computing offers platforms, resources and services that would otherwise be unavailable to them.

At the core of cloud computing are various virtualization technologies and the resulting Virtual Machines (VMs). Virtualization …

Local Selection Of Features And Its Applications To Image Search And Annotation, Jichao Sun

Dissertations

In multimedia applications, direct representations of data objects typically involve hundreds or thousands of features. Given a query object, the similarity between the query object and a database object can be computed as the distance between their feature vectors. The neighborhood of the query object consists of those database objects that are close to the query object. The semantic quality of the neighborhood, which can be measured as the proportion of neighboring objects that share the same class label as the query object, is crucial for many applications, such as content-based image retrieval and automated image annotation. However, due to …

Rice And Mouse Quantitative Phenotype Prediction In Genome-Wide Association Studies With Support Vector Regression, Abdulrhman Fahad M. Aljouie

Theses

Quantitative phenotypes prediction from genotype data is significant for pathogenesis, crop yields, and immunity tests. The scientific community conducted many studies to find unobserved quantitative phenotype high predictive ability models. Early genome-wide association studies (GWAS) focused on genetic variants that are associated with disease or phenotype, however, these variants manly covers small portion of the whole genetic variance, and therefore, the effectiveness of predictions obtained using this information may possibly be circumscribed [ 1 ].

Instead, this study shows prediction ability from whole genome single nucleotide polymorphisms (SNPs) data of 1940 genotyped stoke mouse with - 12k SNPs, and 413 …