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

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, 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

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

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

Doctor of Philosophy, Case Western Reserve University, 2011, EECS - Computer and Information Sciences

Comparative effectiveness, clinical translational research, and personalized medicine depend on the accessibility of data and information. This dissertation introduces an ontology-driven architecture for data federation. Ontological systems such as SNOMED CT have been traditionally used for data exchange, to preserve meaning for data elements across systems. We present a novel method of using ontologies to (1) enable data integration from distributed sources (2) facilitate a federated query interface. The contributions of this dissertation are trifold. First, we developed a unified model consisting of a sleep domain ontology and a units ontology into which data sources can be mapped using an intuitive Data Source to Ontology Mapper (DSOM). The DSOM allows for terminology standardization while accommodating local nomenclature. The domain ontology enriches the expressiveness of complex queries sent across data sources, based on graph traversal and ontological equivalence relationships. We provide further extensions to the ontology framework using formulae, for the purpose of inferring, discovering, and validating data across disparate sources. Second, we developed a powerful query interface called the VISual Aggregator and Explorer (VISAGE) for building and executing federated queries across mapped data sources. The interface streamlines the process of identifying case-control patient populations for study design through the use of embedded statistical tools. A framework is provided for the researcher to choose matching criteria and allows the researcher to create control sets based on frequency matching. Third, we implemented a fully operational and user-friendly system based on the ontology-driven architecture, as part of Physio-MIMI, an NCRR-funded multi-CTSA-site project. Using the Agile methodology to facilitate multi-site collaboration, our implementation leverages a suite of latest software development technologies: Ruby On Rails, Prototype JavaScript, AJAX, an (open full item for complete abstract)

Committee: Guo-Qiang Zhang (Committee Chair); Andy Podgurski (Committee Member); Jing Li (Committee Member); Kenneth Loparo (Committee Member); Satya Sahoo (Committee Member) Subjects: Computer Science