Master of Science, University of Akron, 2006, Computer Science

In today's world of global business, worldwide partnerships and corporate mergers, decision making plays a major role in the steady growth of a business providing it a competitive edge. Decision making is the key to smooth day-to-day operations as well as for effective future planning in this ever competitive world. Several sources of data exist in the business from which valuable information can be extracted to help make a wide range of decisions. In order to facilitate querying and analysis, the data from these sources need to be integrated. There are various considerations and approaches for such Data Integration or Information Integration and several issues surround this process. These issues are considered and the prominent approaches to Information Integration are studied with an emphasis on the Datawarehousing approach. A Datawarehouse is implemented from scratch from available raw data sources and by means of experimentation, it is shown how Datawarehousing is the most suited of all the considered approaches in specific business settings. The main objective of the research and the contribution of this thesis are towards analyzing the major issues faced in specific enterprise scenario and demonstrating how the Datawarehousing approach provides an efficient solution for them and hence provides a solid foundation for Decision Support Systems from distributed, heterogeneous data sources.

Committee: Chien-Chung Chan (Advisor) Subjects:

PhD, University of Cincinnati, 2022, Arts and Sciences: Philosophy

We live in a data-driven world. The brain and mind are being touted as the next target for Big Data. My project here is to evaluate the prospects of a data-driven cognitive science. The central hypothesis of this work is that Big Data heralds fundamental changes to the notion of integration in the philosophy of cognitive science. Although “integration” is seen as a central goal of Big Data research, there is, generally speaking, a tendency to conflate two different senses of integration: the technical issues of making data interoperable, or what we might call data integration, and the theoretical issues in evaluating disparate empirical evidence about the same phenomena, or what we will call information integration. This conflation motivates some of rhetoric surrounding Big Data, particularly in business domains. In cognitive science, as in data science, “integration” is also seen as a central goal, and there is good reason to believe that Big Data technologies may make positive contributions to this goal. One corollary of my central hypothesis is an affirmative response to this. Big Data will indeed contribute to “integration” in cognitive science, though it may not look the way philosophers might have expected. In support of this central hypothesis, I offer three observations on integration in Big Data and its intersection with cognitive science. Specifically: Data integration is presented as a key challenge in large-scale, data-intensive efforts to study the mind-brain. One way in which data integration is achieved is by means of an ontology. “Ontological realism” represents a family of theories about the proper means of data integration via ontologies. I argue here that, at best, what ontological realism demonstrates is a thesis about a set of ontological commitments. With respect to cognitive science, this commitment is neither warranted nor wanted as a normative constraint on data integration. Instead, I recommend a perspectivist approach to dat (open full item for complete abstract)

Committee: Anthony Chemero Ph.D. (Committee Member); Angela Potochnik Ph.D. (Committee Member); Zvi Biener Ph.D. (Committee Member) Subjects: Philosophy

Doctor of Philosophy, University of Toledo, 2022, Industrial Engineering

The main objectives of this dissertation are to investigate the effects of arrests and officers on the Great American Crime Decline, estimate short-term and long-term effects of arrests and policing officers on major crimes, and identify the causal directions between crime, arrests, and officers. Statistical and econometric models such as Fixed Effects Poisson Regression, Panel ARDL Estimation and Panel Granger Causality Testing methods are employed. To avoid spurious regression, tests for cross-section dependency, unit roots, slope-homogeneity and co-integration are conducted to identify the best modeling approaches for effect estimation and causality detection. Data from various sources such as U.S. Census Bureau, F.B.I, Vera Institute of Justice, ICPSR were collected and prepared. In order to carry out a fine-grained analysis, policing agencies are divided into different groups based on population. The dataset for GACD study consisted of 1778 policing agencies from 1990-1999. Arrests of violent, property, disorder, drug sale and possession offenses, and police officers were the predictors while incarceration served as the control variable. For causality study, data on 1553 policing agencies from 1974-2020 was gathered and violent and property arrests, and officers were the independent variables. Results of the GACD study reveal that across all agencies, drug possession and disorder arrests, and officers had deterrence effect on crime, mostly on property crime. Interestingly, officers had a significant deterrence effect on both violent and property crimes only in very large and large agencies. Also, property crimes started to decline at least 3 years earlier than violent crimes. It can be insightful to further examine this delay to understand if property crimes have any effect on violent crimes. From the second study it was observed that both short-term and long-term significant relationships exist between arrests and crime across all agencies. Granger te (open full item for complete abstract)

Committee: Matthew Franchetti Dr. (Committee Chair); Ahalapitiya Jayatissa Dr. (Committee Member); Yue Zhang Dr. (Committee Member); Benjamin George Dr. (Committee Member); Alex Spivak Dr. (Committee Member) Subjects: Criminology; Industrial Engineering; Statistics

Doctor of Philosophy, University of Toledo, 2018, Manufacturing and Technology Management

Today's small-market and mid-market sized manufacturers, competitively face increasing pressure to capture, integrate, operationalize, and manage diverse sources of digitized data. Many have made significant investments in data technologies with the objective to improve on organization performance yet not all have realized demonstrable benefits that create organization value. One simple question arises, do business-analytics make a difference on company performance in today's information intensive environment? The research purpose, to explore this question by looking through the lens of data-centric pressure placed on management driving the invested use of data-technologies; how these drivers impact on management influence to adopt a digitized organization mindset, effecting data practices, shaping key processes and strategies and leading to capabilities growth that impact on performance and culture. The terms `Big Data' and `Small Data' are two of the most prolific used phrases in today's world when discussing business analytics and the value data provides on organization performance. Big Data, being strategic to organization decision-making, and Small Data, operational; is captured from a host of internal and external sources. Studying how leveraging business-analytics into organizational value is of research benefit to both academic and practioner audiences alike. The research on `Big and Small Data, and business analytics' is both varied and deep and originating from a host of academic and non-academic sources; however, few empirical studies deeply examine the phenomena as experienced in the manufacturing environment. Exploring the pressures managers face in adopting data-centric managing beliefs, applied practices, understanding key value-creating process strategy mechanisms impacting on the organization, thus provides generalizable insights contributing to the pool of knowledge on the importance of data-technology investments impacting on organizational cul (open full item for complete abstract)

Committee: Paul Hong (Committee Chair); Thomas Sharkey (Committee Member); Wallace Steven (Committee Member); Cheng An Chung (Committee Member) Subjects: Information Systems; Information Technology; Management; Organization Theory; Organizational Behavior

Doctor of Philosophy (PhD), Wright State University, 2015, Computer Science and Engineering PhD

Data integration is concerned with providing a unified access to data residing at multiple sources. Such a unified access is realized by having a global schema and a set of mappings between the global schema and the local schemas of each data source, which specify how user queries at the global schema can be translated into queries at the local schemas. Data sources are typically developed and maintained independently, and thus, highly heterogeneous. This causes difficulties in integration because of the lack of interoperability in the aspect of architecture, data format, as well as syntax and semantics of the data. This dissertation represents a study on how small, self-contained ontologies, called ontology design patterns, can be employed to provide semantic interoperability in a cross-repository data integration system. The idea of this so-called ontology pattern- based data integration is that a collection of ontology design patterns can act as the global schema that still contains sufficient semantics, but is also flexible and simple enough to be used by linked data providers. On the one side, this differs from existing ontology-based solutions, which are based on large, monolithic ontologies that provide very rich semantics, but enforce too restrictive ontological choices, hence are shunned by many data providers. On the other side, this also differs from the purely linked data based solutions, which do offer simplicity and flexibility in data publishing, but too little in terms of semantic interoperability. We demonstrate the feasibility of this idea through the actual development of a large scale data integration project involving seven ocean science data repositories from five institutions in the U.S. In addition, we make two contributions as part of this dissertation work, which also play crucial roles in the aforementioned data integration project. First, we develop a collection of more than a dozen ontology design patterns that capture the key noti (open full item for complete abstract)

Committee: Pascal Hitzler Ph.D. (Advisor); Krzysztof Janowicz Ph.D. (Committee Member); Khrisnaprasad Thirunarayan Ph.D. (Committee Member); Michelle Cheatham Ph.D. (Committee Member) Subjects: Computer Science; Information Systems; Information Technology; Logic

Master of Science (MS), Ohio University, 2014, Industrial and Systems Engineering (Engineering and Technology)

Product designers produce the design of the product to satisfy the product specifications by applying their own design intent. Due to the lack of semantic capability of the modern Computer aided Design (CAD) applications and standard formats (STEP) to store design data, the design intent of the designer is lost and only geometrical information remain in the design data (non-semantics aware design). Unavailability of the design purpose or the significance of the design in the design data makes It hard to integrate manufacturing design data with other applications of Computer Integrated Manufacturing (CIM). For this reason, it is important to interpret functional properties of design data. In this thesis, a structural analysis process is developed, which uses a novel algorithm from computational geometry to extract Degrees of Freedoms of each part in an assembly. These information is used to recognize translational properties of the parts. Along with it, functional design of a conceptual system, called SIDOS, is presented, which compares the expected behaviors, derived from the functional behaviors, which are in turn extracted from a non-semantics aware design, with the expected behaviors of a semantics-aware template design from the same product family to identify similar components between them and annotates the components of the non-semantics aware design with the matching semantics from the template design.

Committee: David Koonce (Advisor); Dusan Sormaz (Committee Member); Robert Judd (Committee Member); Gursel Suer (Committee Member); Vic Matta (Committee Member) Subjects: Industrial Engineering

PhD, University of Cincinnati, 2014, Engineering and Applied Science: Computer Science and Engineering

With the continuing explosive growth of the world wide web, a wealth of information has become available online. The web has become one of the major sources of information for both individual users and large organizations. To find the information, individual users can either use search engines or navigate to a particular website following links. The former method returns links to vast amounts of data in seconds while the latter one could be tedious and time consuming. The presentation of results using the former method is usually a web page with links to actual web data sources (or websites). The latter method takes the user to the actual web data source itself. Using the two most popular forms of web data presentation/retrieval, web data can hardly be queried, manipulated and analyzed easily even though it is publicly and readily available. Many companies also use web for information whose challenge is to build web-based analytical and decision support systems, often referred to as web data warehouses. However, the information present on the web is extremely complex and heterogeneous which brings along with it a challenge in integrating and presenting retrieved web data in a uniform format. Hence, there is a need for different web data integration frameworks that can integrate and present web data in a uniform format. To achieve a homogeneous representation of web data we need a framework that extracts relevant structured and semi-structured web data from different web data sources, generates schemas from structured as well as semi-structured web data, and integrates schemas generated from different structured and semi-structured web data sources into a merged schema, populates it with data and presents it to the end user in a uniform format. We propose a modular framework for homogeneous presentation of web data. This framework consists of different standalone modules that can also be used to create independent systems that solve other schema unification problem (open full item for complete abstract)

Committee: Karen Davis Ph.D. (Committee Chair); Raj Bhatnagar Ph.D. (Committee Member); Hsiang-Li Chiang Ph.D. (Committee Member); Ali Minai Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2009, Computer Science and Engineering PhD

A Grid is an integration infrastructure for sharing and coordinated use of diverseresources in dynamic, distributed virtual organizations (VOs). A Data Grid is an architecture for the access, exchange, and sharing of data in the Grid environment. Distributed data resources can be diverse in their formats, schema, quality, access mechanisms, ownership, access policies, and capabilities. In recent years, several organizations have started utilizing Grid technologies to deploy data-intensive and/or computation-intensive applications. As more and more organizations are sharing data resources and participating in Data Grids, the complexity and heterogeneity of the systems is increasing constantly, but their management techniques are not evolving making the systems more complicated and error-prone, indicating a clear need for standardized mechanisms to manage access control for the shared data resources. The Open Grid Services Architecture - Data Access and Integration (OGSA-DAI) and the Storage Resource Broker (SRB) are widely used frameworks for the integration of heterogeneous data resources in Data Grid systems. However, in these systems, access control causes substantial administration overhead for the resource providers because the authorization information has to be maintained for individual Grid users. In addition, access control policies need to specified and managed across multiple organizations. And, each organization in a Data Grid may use its own terminology to describe a resource making it difficult to coordinate between the organizations. This dissertation focuses on solving these problems and provides access control systems that are based on existing standards. We developed a role-based access control (RBAC) system with Shibboleth, which is an attribute authorization service currently being used in many Grid applications. We used the Core and Hierarchical RBAC profile of the eXtensible Access Control Markup Language (XACML) standard for specifying access c (open full item for complete abstract)

Committee: Soon M. Chung PhD (Advisor); Nikolaos Bourbakis PhD (Committee Member); Yong Pei PhD (Committee Member); Xinhui Zhang PhD (Committee Member); Michael Talbert PhD (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2007, Computer Science and Engineering PhD

Grid has emerged recently as an integration infrastructure for the sharing and coordinated use of diverse resources in dynamic, distributed virtual organizations (VOs). A Data Grid is an architecture for the access, exchange, and sharing of data in the Grid environment. In this dissertation, role-based access control (RBAC) systems for heterogeneous data resources in Data Grid systems are proposed. The Open Grid Services Architecture – Data Access and Integration (OGSA-DAI) is a widely used framework for the integration of heterogeneous data resources in Grid systems. However, in the OGSA-DAI system, access control causes substantial administration overhead for resource providers in VOs because each of them has to manage the authorization information for individual Grid users. Its identity-based access control mechanisms are severely inefficient and too complicated to manage because the direct mapping between users and privileges is transitory. To solve this problem, (1) the Community Authorization Service (CAS), provided by the Globus toolkit, and (2) the Shibboleth, an attribute authorization service, are used to support RBAC in the OGSA-DAI system. The Globus Toolkit is widely used software for building Grid systems. Access control policies need to be specified and managed across multiple VOs. For this purpose, the Core and Hierarchical RBAC profile of the eXtensible Access Control Markup Language (XACML) is used; and for distributed administration of those policies, the Object, Metadata and Artifacts Registry (OMAR) is used. OMAR is based on the e-business eXtensible Markup Language (ebXML) registry specifications developed to achieve interoperable registries and repositories. The RBAC systems allow quick and easy deployments, privacy protection, and the centralized and distributed management of privileges. They support scalable, interoperable and fine-grain access control services; dynamic delegation of rights; and user-role assignments. They also reduce the (open full item for complete abstract)

Committee: Soon Chung (Advisor) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2010, Computer Science and Engineering

A popular trend in data dissemination involves online data sources that are hidden behind query forms, thus forming what is referred to as the deep web. Deep web data is stored in hidden databases. Hidden data can only be acessed after a user submits a query by filling an online form. Currently, hundreds of large, complex and in many cases, related and/or overlapping, deep web data sources have become available. The number of such data sources is still increasing rapidly every year. The emergence of the deep web is posing many new challenges in data integration and query answering. First, the metadata of the deep web and the data records stored in deep web databases are hidden from the data integration system. Second, Multiple deep web data sources may have data redundancy. Furthermore, similar data sources may provide data with different data quality and even conflicting data. Therefore, data source selection is of great importance for a data integration system. Third, deep web data sources in a domain often have inter-dependencies, i.e., the output from one data source may be the input of another data source. Thus, answering a query over a set of deep web data sources often involving accessing a sequence of inter-dependent data sources in an intelligent order. Fourth, the common way of accessing data in deep web data sources is through standardized input interfaces. These interfaces, on one hand, provide a very simple query mechanism. On the other hand, these interfaces significantly constrain the types of queries that could be automatically executed. Finally, all deep web data sources are network based. Both the data source servers and network links are vulnerable to congestion and failures. Therefore, handling with fault tolerance issue is also necessary for a data integration system. In our work, we propose SEEDEEP, an automatic system for exploring and querying deep web data sources. The SEEDEEP system is able to integrate deep web data sources in a particular (open full item for complete abstract)

Committee: Gagan Agrawal PhD (Advisor); Feng Qin PhD (Committee Member); P Sadayappan PhD (Committee Member) Subjects: Computer Science

MS, University of Cincinnati, 2024, Engineering and Applied Science: Computer Science

Brain tumor segmentation is crucial for diagnosis, treatment planning, and patient monitoring. Traditional manual segmentation is labor-intensive and prone to variability, necessitating the development of automated, precise, and reproducible methods. This study enhances segmentation by integrating multi-modal MRI scans (T1-weighted, T2-weighted, FLAIR, and post-contrast T1-weighted) with clinical data using advanced deep learning techniques. Multi-modal MRI provides diverse tissue contrasts essential for identifying tumor regions like the enhancing core, peritumoral edema, and necrosis. Incorporating clinical data—such as patient age, survival days, and genetic markers—adds context influencing tumor appearance and growth patterns, refining the segmentation process. The proposed framework uses convolutional neural networks (CNNs), particularly U-Net architectures, trained on datasets like the BraTS Challenge, with data augmentation and cross-validation enhancing robustness and generalizability. A multi-input model processes imaging data and clinical features through parallel neural network branches, fusing them to form a comprehensive representation. This integration captures complex relationships between clinical variables and imaging features, improving segmentation outcomes. Performance is evaluated using metrics such as Dice Similarity Coefficient (DSC), Accuracy, and Intersection over Union. Initial results show that combining multimodal MRI with clinical data significantly improves segmentation accuracy and delineation of tumor boundaries. In conclusion, integrating multi-modal MRI and clinical data in brain tumor segmentation offers more accurate and clinically meaningful results. This approach harnesses the full spectrum of imaging information and contextual clinical insights, paving the way for more effective and personalized patient care. Future work will refine the model architecture, expand (open full item for complete abstract)

Committee: Vikram Ravindra Ph.D. (Committee Chair); FNU Nitin Ph.D. (Committee Member); Jun Bai Ph.D. (Committee Member) Subjects: Computer Science

PhD, University of Cincinnati, 2024, Arts and Sciences: Statistics

In this dissertation, two innovative statistical methodologies are developed to enhance the accuracy of climate model projections. Climate models simulate future global climate conditions but are constrained by coarse resolutions due to computational limitations. Consequently, these projections must be refined to finer resolutions before they can be effectively utilized in regional studies. The first methodology introduces a novel spatial statistical model for downscaling climate model projections. This approach significantly enhances precision by incorporating spatial dependencies and stands out by providing meaningful uncertainty estimates, a feature often missing in many previous downscaling approaches. Additionally, the method achieves computational efficiency through a basis representation, making it adept at managing large datasets effectively. Furthermore, climate models originate from various research groups, each based on different understandings and assumptions about the Earth's climate. This leads to significant uncertainty in the choice of models for subsequent analysis. Since there is no definitive way to select the best model or a few reliable ones, climate scientists often seek methods to combine projections from multiple models to mitigate this uncertainty. The second method in this dissertation introduces a comprehensive approach to integrate projections from multiple climate models using Bayesian Model Averaging (BMA). The proposed method effectively tackles the challenge of implementing BMA for climate model integration in a full Bayesian framework by employing Polya-Gamma augmentation and yields combined climate projections with improved accuracy and reliable uncertainty estimates.

Committee: Emily Kang Ph.D. (Committee Chair); Bledar Konomi Ph.D. (Committee Member); Won Chang Ph.D. (Committee Member) Subjects: Statistics

Doctor of Philosophy, The Ohio State University, 2023, Computer Science and Engineering

In many biomedical contexts, multiple types of BDMs (e.g., metabolites, genes, proteins, chromatin states, and DNA methylation sites) associate with one another directly or indirectly in groups or chains to impact phenotype or outcome. Certain significant associations often help in data interpretation and novel hypotheses generation, motivating researchers to identify the most impactful groups of BDM associations between multiple types of data. However, many state-of-the-art models focus either on individual BDM associations independently of one another or implement black box predictors of outcome that are agnostic of BDM associations. Moreover, collection of multiple types of BDMs in a subject (i.e., multi-omics data) is not always feasible, motivating the need to infer one omic type of data from another. This dissertation tackles the related problems of (1) using inter-omics approaches to infer BDM types from other related BDM types in specific contexts, (2) finding groups of multi-omics data BDMs associated with outcome through multivariate statistical analysis and graph-based predictive models, and (3) interpreting groups of multi-omics data BDMs associated with outcome in a functional context using existing knowledge. This dissertation addresses the problem of using inter-omics approaches to infer BDM types from other related BDM types in two domains of note: (1) regulatory element annotation, and (2) protein abundance prediction. First, this dissertation introduces the Self Organizing Map with Variable Neighborhoods (SOM-VN), designed to annotate regulatory elements across whole human genomes using shapes found in chromatin accessibility assays. The novelty of SOM-VN is that, while most computational tools for annotating regulatory elements require a suite of resource-intensive experimental assays, SOM-VN uses only a single assay to annotate regulatory elements. SOM-VN is validated on chromatin accessibility assays from multiple H1, HeLa, A549, and GM12878 ce (open full item for complete abstract)

Committee: Raghu Machiraju (Advisor); Ewy Mathé (Advisor); Andrew Perrault (Committee Member); Rachel Kopec (Committee Member); Rachel Kelly (Committee Member) Subjects: Applied Mathematics; Artificial Intelligence; Bioinformatics; Biomedical Research; Biostatistics; Computer Science

Master of Sciences, Case Western Reserve University, 2024, Materials Science and Engineering

Advanced analytics of diverse geospatial data streams can provide invaluable insights into complex agricultural and environmental systems. This work pioneers an integrated spatiotemporal analysis approach synthesizing satellite imagery, digital soil mapping, hydrological measurements, elevation, and historical crop data. The overarching objective of this work is to quantify relationships between crop growth dynamics, soil properties, nutrient distribution, water quality, and land use patterns. The methodology employs a case study focused on major agricultural regions to show the potential of big data analytic techniques. Custom applications and tools built on distributed computing infrastructure enable the assimilating and processing of massive heterogeneous datasets. The results unveil intricate connections between vegetation indices, soil nutrients, crop types, and nutrient transport, offering strategic perspectives to enhance productivity while minimizing environmental impacts. The multi-faceted understanding achieved fills critical knowledge gaps regarding interactions within agroecosystems. While moderate-resolution regional data provided informative baseline insights, higher spatiotemporal resolution and expanded geographic scope would further strengthen the analysis. Overall, this work underscores the immense potential of data science, geospatiotemporal analytics, and systems thinking to address pressing crop, land, nutrient, water, and soil challenges. The integrated approach provides a powerful paradigm for leveraging emerging data streams toward creating a digital agriculture ecosystem.

Committee: Roger H. French (Advisor); Jeffrey M Yarus (Advisor); Pawan K. Tripathi (Committee Member); Yinghui Wu (Committee Member); Erika I. Barcelos (Committee Member); Alp Sehirilioglu (Committee Member) Subjects: Agriculture; Computer Science; Engineering; Environmental Geology; Environmental Studies; Food Science; Geotechnology; Materials Science

Doctor of Philosophy, Case Western Reserve University, 2017, EECS - Computer Engineering

In this era of technological transformation in medicine, there is need to revolutionize the approach and procedures involved in the treatment of diseases to have a restructured understanding of the role of data and technology in the medical industry. Data is a key factor in diagnosis, management, and treatment of patients in any medical institution. Proper management and usage of patient's data will go a long way in helping the society save money, time and life of the patient. Having data is one thing and providing a system or means of translating the data is another issue. This dissertation is proposing a design of a Point of Care system for the Intensive Care Unit (a.k.a ICU_POC), which is a system that integrates the capabilities of the bedside monitors, bedside eFlowsheet and the Electronic Medical Records in such a manner that the clinicians interact with one another in real time from different locations, to view, analyze, and even make necessary diagnoses on patients' ailment based on their medical records. It demonstrates how patient data from the monitors can be imported, processed, and transformed into meaningful and useful information, stored, reproduced and transferred automatically to all necessary locations securely and efficiently without any human manipulation. ICU_POC will grant physicians the remote capability in managing patients properly by providing accurate patient data, easy analysis and fast diagnosis of patient conditions. It creates an interface for physicians to query historical data and make proper assumptions based on previous medical conditions. The problem lies in managing data transfer securely between one hospital EMR database and the other for easy accessibility of data by the physicians. This work is challenged by designing a system that could provide a fast, accurate, secure and effective (FASE) diagnosis of medical conditions of the patients in the ICU. The proposed system has the potential of reducing patients' length of stay i (open full item for complete abstract)

Committee: Kenneth Loparo (Advisor); Farhad Kaffashi (Committee Member); Vira Chankong (Committee Member); Michael Degeorgia (Committee Member) Subjects: Computer Engineering; Computer Science; Engineering

Doctor of Philosophy (PhD), Wright State University, 2014, Computer Science and Engineering PhD

Ontology alignment is an important step in enabling computers to query and reason across the many linked datasets on the semantic web. This is a difficult challenge because the ontologies underlying different linked datasets can vary in terms of subject area coverage, level of abstraction, ontology modeling philosophy, and even language. The alignment approach presented here centers on string similarity metrics. Nearly all ontology alignment systems use a string similarity metric in one form or another, but it seems that the choice of a particular metric is often arbitrary. We begin this dissertation with the most comprehensive survey to date on the performance of string similarity metrics and string preprocessing strategies for ontology alignment. Based on this work we present practical guidelines for choosing string metrics in the face of different types of ontologies and different alignment goals. Additionally, we show that string similarity metrics alone can perform competitively with state-of-the-art alignment systems on the most popular benchmarks in the field. One of the contributions of our string similarity metric survey is quantification of the difference in performance between aligning classes and aligning properties (relations between classes). Put simply: aligning properties is hard, and existing string similarity metrics are not of great help. We therefore take on the task of developing a new string-based alignment approach that performs better on properties. Unfortunately, evaluating that approach is difficult because the only existing alignment benchmark that includes properties is, in our view, unrealistic since all relations in the reference alignment are presented as completely certain. Human experts do not have this degree of confidence when asked to align an ontology. We therefore present a more nuanced version of this benchmark that we have created through a combination of expert survey and crowdsourcing. We then present our new string-base (open full item for complete abstract)

Committee: Pascal Hitzler Ph.D. (Advisor); Isabel Cruz Ph.D. (Committee Member); Krishnaprasad Thirunarayan Ph.D. (Committee Member); Mateen Rizki Ph.D. (Committee Member) Subjects: Computer Science

PhD, University of Cincinnati, 2014, Engineering and Applied Science: Computer Science and Engineering

Machine learning is a branch of generic artificial intelligence, which covers a wide range of learning topics. A variety of supervised and unsupervised models of machine learning/data mining have been applied extensively in biomedical informatics studies for knowledge discovery. Advantages of meta-analysis or data fusion have been discussed in many research domains. Specifically, growing data, information, and knowledge covering various dimensions of human development and diseases calls for efficient integrative and mining efforts to analyze such heterogeneous information simultaneously. In this dissertation, we present our work to extract hidden knowledge from data about the large-scale complex biological systems that usually involve heterogeneous entities and associations between them. First, we propose a biclustering algorithm to identify entities that may manifest cohesiveness within a subspace of conditions. We apply this algorithm to predict combinatorial regulation of transcription factors. We also extend the algorithm to generate 3-clusters in order to capture associations between different classes of entities. Second, we propose network-based approaches to predict drug repositioning candidates. These computational models utilize heterogeneous genomic and pharmacological information to generate potential drug repositioning candidates. We validate the approach using known indications before applying to predict new indications for existing drugs. Third, we study several statistical and computational strategies to generate overall significance of relationships between different biological entities. We apply this specifically to the problem of microRNA target ranking. We propose a framework that applies a series of data mining methods to prioritize entities in a heterogeneous network context. We also develop a workbench ToppMiR based on this framework to infer significant microRNAs and mRNA targets given a biological context.

Committee: Raj Bhatnagar Ph.D. (Committee Chair); Anil Jegga D.V.M. M.Res. (Committee Member); Bruce Aronow Ph.D. (Committee Member); Yizong Cheng Ph.D. (Committee Member); Ali Minai Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2014, Geodetic Science and Surveying

Shoreline mapping has been a critical task for federal/state agencies and coastal communities. It supports important applications such as nautical charting, coastal zone management, and legal boundary determination. Current attempts to incorporate data from hyperspectral imagery to increase the efficiency and efficacy of shoreline mapping have been limited due to the complexity in processing its data as well as its inferior spatial resolution when compared to multispectral imagery or to sensors such as LiDAR. As advancements in remote-sensing technologies increase sensor capabilities, the ability to exploit the spectral formation carried in hyperspectral images becomes more imperative. This work employs a new approach to extracting shorelines from AVIRIS hyperspectral images by combination with a LiDAR-based DEM using a multiphase active contour segmentation technique. Several techniques, such as study of object spectra and knowledge-based segmentation for initial contour generation, have been employed in order to achieve a sub-pixel level of accuracy and maintain low computational expenses. Introducing a DEM into hyperspectral image segmentation proves to be a useful tool to eliminate misclassifications and improve shoreline positional accuracy. Experimental results show that mapping shorelines from hyperspectral imagery and a DEM can be a promising approach as many further applications can be developed to exploit the rich information found in hyperspectral imagery.

Committee: Alper Yilmaz Ph.D. (Advisor); Alan Saalfeld Ph.D. (Committee Member); von Frese Ralph Ph.D. (Committee Member) Subjects: Geographic Information Science; Remote Sensing

PhD, University of Cincinnati, 2007, Engineering : Biomedical Engineering

An important goal for biomedical research is to elucidate causal and modifier networks of human disease. While integrative functional genomics approaches have shown success in the identification of biological modules associated with normal and disease states, a critical bottleneck is representing knowledge capable of encompassing asserted or derivable causality mechanisms. Both single gene and more complex multifactorial diseases often exhibit several phenotypes and a variety of approaches suggest that phenotypic similarity between diseases can be a reflection of shared activities of common biological modules composed of interacting or functionally related genes. Thus, analyzing the overlaps and interrelationships of clinical manifestations of a series of related diseases may provide a window into the complex biological modules that lead to a disease phenotype. In order to evaluate our hypothesis, we are developing a systematic and formal approach to extract phenotypic information present in textual form within Online Mendelian Inheritance in Man (OMIM) and Syndrome DB databases to construct a disease - clinical phenotypic feature matrix to be used by various clustering procedures to find similarity between diseases. Our objective is to demonstrate relationships detectable across a range of disease concept types modeled in UMLS to analyze the detectable clinical overlaps of several Cardiovascular Syndromes (CVS) in OMIM in order to find the associations between phenotypic clusters and the functions of underlying genes and pathways. Most of the current biomedical knowledge is spread across different databases in different formats and mining these datasets leads to large and unmanageable results. Semantic Web principles and standards provide an ideal platform to integrate such heterogeneous information and could allow the detection of implicit relations and the formulation of interesting hypotheses. We implemented a page-ranking algorithm onto Semantic Web to prioriti (open full item for complete abstract)

Committee: Dr. Bruce Aronow (Advisor) Subjects:

Master of Science, The Ohio State University, 2012, Computer Science and Engineering

There is a tremendous change in the amount of electronic data available to us and the manner in which we use it. With the on going “Big Data” movement we are facing the challenge of data “volume, variety and velocity.” The linked data movement and semantic web technologies try to address the issue of data variety. The current demand for advanced data analytics and services have triggered the shift from data services to knowledge services and delivery platforms. Semantics plays a major role in providing richer and more comprehensive knowledge services. We need a stable, sustainable, scalable and verifiable framework for knowledge-based semantic services. We also need a way to validate the “semantic” nature of such services using this framework. Just having a framework is not enough. The usability of this framework should be tested with a good example of a semantic service as a case study in a key research domain. The thesis addresses two research problems. Problem 1: A generalized framework for the development of end-to-end semantic services needs to be established. The thesis proposes such a framework that provides architecture for developing end–to–end semantic services and metrics for measuring its semantic nature. Problem 2: To implement a robust knowledge based service using the architecture proposed by the semantic service framework and its semantic nature can be validated using the proposed framework. ResearchIQ, a semantic search portal for resource discovery in the biomedical research domain, has been implemented. It is intended to serve as the required case study for testing the framework. The architecture of the system follows the design principles of the proposed framework. The ResearchIQ system is truly semantic from end-to-end. The baseline evaluation metrics of the said framework are used to prove this claim. Several key data sources have been integrated in the first version of the ResearchIQ system. It serves as a framework for semantic data integrat (open full item for complete abstract)

Committee: Jayashree Ramanathan PhD (Advisor); Rajiv Ramnath PhD (Committee Member) Subjects: Biomedical Research; Computer Engineering; Computer Science; Information Science; Information Systems; Information Technology