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Today, databases have become an integral part of information systems. In the past two decades, we have seen different database systems being developed independently and used in different applications domains. Today's interconnected networks and advanced applications, such as data warehousing, data mining & knowledge discovery and intelligent data access to information on the Web, have created a need for integrated access to such heterogeneous, autonomous, distributed database systems. Heterogeneous/multidatabase research has focused on this issue resulting in many different approaches. However, a single, generally accepted methodology in academia or industry has not emerged providing ubiquitous intelligent data access from heterogeneous, autonomous, distributed information sources. This thesis describes a heterogeneous database system being developed at Highperformance Database Research Center (HPDRC). A major impediment to ubiquitous deployment of multidatabase technology is the difficulty in resolving semantic heterogeneity. That is, identifying related information sources for integration and querying purposes. Our approach considers the semantics of the meta-data constructs in resolving this issue. The major contributions of the thesis work include: (i.) providing a scalable, easy-to-implement architecture for developing a heterogeneous multidatabase system, utilizing Semantic Binary Object-oriented Data Model (Sem-ODM) and Semantic SQL query language to capture the semantics of the data sources being integrated and to provide an easy-to-use query facility; (ii.) a methodology for semantic heterogeneity resolution by investigating into the extents of the meta-data constructs of component schemas. This methodology is shown to be correct, complete and unambiguous; (iii.) a semi-automated technique for identifying semantic relations, which is the basis of semantic knowledge for integration and querying, using shared ontologies for context-mediation; (iv.) resolutions for schematic conflicts and a language for defining global views from a set of component Sem-ODM schemas; (v.) design of a knowledge base for storing and manipulating meta-data and knowledge acquired during the integration process. This knowledge base acts as the interface between integration and query processing modules; (vi.) techniques for Semantic SQL query processing and optimization based on semantic knowledge in a heterogeneous database environment; and (vii.) a framework for intelligent computing and communication on the Internet applying the concepts of our work.

Since the introduction of Bitcoin, numerous studies on Bitcoin mining attacks have been conducted, and as a result, many countermeasures to these attacks have been proposed. The reputation-based mining paradigm is a comprehensive countermeasure solution to this problem with the goal of regulating the mining process and preventing mining attacks. This is accomplished by incentivizing miners to avoid dishonest mining strategies using reward and punishment mechanisms. This model was validated solely based on game theoretical analyses and the real-world implications of this model are not known due to the lack of empirical data. To shed light on this issue, we designed a simulated mining platform to examine the effectiveness of the reputation-based mining paradigm through data analysis. We implemented block withholding attacks in our simulation and ran the following three scenarios: Reputation mode, non-reputation mode, and no attack mode. By comparing the results from these three scenarios, interestingly we found that the reputation-based mining paradigm decreases the number of block withholding attacks, and as a result, the actual revenue of individual miners becomes closer to their theoretical expected revenue. In addition, we observed that the confidence interval test can effectively detect block withholding attacks however, the test also results in a small number of false positive cases. Since the effectiveness of the reputation-based model relies on attack detection, further research is needed to investigate the effect of this model on other dishonest mining strategies. / Includes bibliography. / Thesis (MS)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Effective communication among team members in software development projects is increasingly significant for the success of the project. Successful software projects are the catalyst for achieving profitability objectives and creating shareholder value in organizations. The purpose of this single case study was to investigate communication strategies information technology (IT) project managers used for successful team collaboration in software development. The population for this study comprised senior IT project managers. The project managers had supervision responsibilities from a midsized IT company in Alberta, Canada. The sociotechnical theory guided this study as the conceptual framework. Data were collected from semistructured interviews with 13 senior IT project managers on their experiences using effective communication strategies for team collaboration. A review of 11 company documents was conducted. Using methodological triangulation and member checking of original interview transcripts served to establish the trustworthiness of final interpretations. Through thematic analysis, 4 significant themes emerged from the study: effective communication, attributes of communication, the importance of social and emotional intelligence, and the impact of postwork activities for team collaboration. The findings of this study might bring about positive change by supporting senior project managers use of communication strategies for team collaborations in midsize IT companies to increase job satisfaction and project completion.

Phishing scams which use emails to trick users into revealing personal data have become pandemic in the world. Analyzing such emails to extract maximum information about them and make intelligent forensic decisions based on such an analysis is a major task for law enforcement agencies. To date such analysis is done by manually checking various headers of a raw email and running various Unix tools on its constituent parts such as IP addresses, links, domain names. This thesis describes the design and development of a database system used for automation of a system called the Undercover Multipurpose Anti-Spoofing Kit (UnMASK) that will enable investigators to reduce the time and effort needed for digital forensic investigations of email-based crimes. It also describes how the database is used to perform such automation. UnMASK uses a database for organizing a work flow to automatically launch Unix tools to collect additional information from the Internet. The retrieved information is in turn added to the database. UnMASK is a working system. To the best of our knowledge, UnMASK is the first comprehensive system that can automate the process of analyzing emails using a database and then generate forensic reports that can be used for subsequent investigation and prosecution. / A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Masters of Science. / Degree Awarded: Fall Semester, 2007.. / Date of Defense: October 10, 2007. / Database Automation, Automating Email Analysis, Email Analysis, Automation using a Database / Includes bibliographical references. / Sudhir Aggarwal, Professor Directing Thesis; Zhenhai Duan, Committee Member; Piyush Kumar, Committee Member.

Technological advancements have lead to the generation of enormous amounts ofdata. In order to capitalize on this trend, however, both computational and sta-tistical challenges must be tackled. While computational efficiency is important,interpretability of models and algorithms are essential to ensuring the validity of anyconclusions drawn. Nowhere is this more clear than in the case of biomedical data,where inferences drawn from large datasets are used to inform future directions ofresearch, diagnose diseases, and generate leads for the development of new pharma-ceuticals. This work examines the interplay between statistical concepts and compu-tational models in three applications. Specifically, quantifying protein expression offluorescent images, classifying somatic mutations in cancer, and combining p-valuescomputed from genomic summary statistics. Across these applications, there are threerecurring themes: accounting for technical and biological variation in data process-ing, evaluating the performance of a model in its end use case, and integrating resultswith outside data. Within these applications and themes, many statistical conceptsare employed including Bayes theorem, and type I error rate control alongside com-putational models such a convolutional neural networks and Monte Carlo samplingalgorithms. The results of these investigations inform much broader application ar-eas such as biomedical imaging, modeling genomic sequences, and hypothesis testingin high-dimensions. Specific contributions in the application of Convolutional NeuralNetworks include demonstrating their ability to replicate the quantification of proteinexpression images from various manually-generated or deterministic label sets as wellas the creation of a modeling framework for sequencing-based cancer diagnostics and the prioritization of unvalidated somatic mutations. In the area of hypothesis test-ing, novel algorithms are proposed that enable the use of a powerful and interpretabletechnique of combining p-values in the large-scale setting of genome-wide association studies.

Submitted to the faculty of the Chemical Informatics Graduate Program in partial fulfillment of the requirements for the degree Master of Science in the School of Informatics,Indiana University, December 2005 / The completion of Human Genome Project is seen as a gateway to the discovery of novel drug targets (Jacoby, Schuffenhauer, & Floersheim, 2003). How much of this information is actually translated into knowledge, e.g., the discovery of novel drug targets, is yet to be seen. The traditional route of drug discovery has been from target to compound. Conventional research techniques are focused around studying animal and cellular models which is followed by the development of a chemical concept. Modern approaches that have evolved as a result of progress in molecular biology and genomics start out with molecular targets which usually originate from the discovery of a new gene .Subsequent target validation to establish suitability as a drug target is followed by high throughput screening assays in order to identify new active chemical entities (Hofbauer, 1997). In contrast, chemogenomics takes the opposite approach to drug discovery (Jacoby, Schuffenhauer, & Floersheim, 2003). It puts to the forefront chemical entities as probes to study their effects on biological targets and then links these effects to the genetic pathways of these targets (Figure 1a). The goal of chemogenomics is to rapidly identify new drug molecules and drug targets by establishing chemical and biological connections. Just as classical genetic experiments are classified into forward and reverse, experimental chemogenomics methods can be distinguished as forward and reverse depending on the direction of investigative process i.e. from phenotype to target or from target to phenotype respectively (Jacoby, Schuffenhauer, & Floersheim, 2003). The identification and characterization of protein targets are critical bottlenecks in forward chemogenomics experiments. Currently, methods such as affinity matrix purification (Taunton, Hassig, & Schreiber, 1996) and phage display (Sche, McKenzie, White, & Austin, 1999) are used to determine targets for compounds. None of the current techniques used for target identification after the initial screening are efficient. In silico methods can provide complementary and efficient ways to predict targets by using chemogenomics databases to obtain information about chemical structures and target activities of compounds. Annotated chemogenomics databases integrate chemical and biological domains and can provide a powerful tool to predict and validate new targets for compounds with unknown effects (Figure 1b). A chemogenomics database contains both chemical properties and biological activities associated with a compound. The MDL Drug Data Report (MDDR) (Molecular Design Ltd., San Leandro, California) is one of the well known and widely used databases that contains chemical structures and corresponding biological activities of drug like compounds. The relevance and quality of information that can be derived from these databases depends on their annotation schemes as well as the methods that are used for mining this data. In recent years chemists and biologist have used such databases to carry out similarity searches and lookup biological activities for compounds that are similar to the probe molecules for a given assay. With the emergence of new chemogenomics databases that follow a well-structured and consistent annotation scheme, new automated target prediction methods are possible that can give insights to the biological world based on structural similarity between compounds. The usefulness of such databases lies not only in predicting targets, but also in establishing the genetic connections of the targets discovered, as a consequence of the prediction. The ability to perform automated target prediction relies heavily on a synergy of very recent technologies, which includes: i) Highly structured and consistently annotated chemogenomics databases. Many such databases have surfaced very recently; WOMBAT (Sunset Molecular Discovery LLC, Santa Fe, New Mexico), KinaseChemBioBase (Jubilant Biosys Ltd., Bangalore, India) and StARLITe (Inpharmatica Ltd., London, UK), to name a few. ii) Chemical descriptors (Xue & Bajorath, 2000) that capture the structure-activity relationship of the molecules as well as computational techniques (Kitchen, Stahura, & Bajorath, 2004) that are specifically tailored to extract information from these descriptors. iii) Data pipelining environments that are fast, integrate multiple computational steps, and support large datasets. A combination of all these technologies may be employed to bridge the gap between chemical and biological domains which remains a challenge in the pharmaceutical industry.

The majority of organizations worldwide have adopted IT consumerization. However, only a small percentage of them explicitly manage the dual use of personal devices and applications for work purposes. This correlational study used the extended unified technology acceptance and use technology model (UTAUT2) to examine whether employees' perceptions of habit, effort expectancy, performance expectancy, facilitating conditions, hedonic motivation, social influence, and price value can predict IT consumerization behavioral intentions (BI). A pre-existing UTAUT2 survey instrument was used to collect data from employees (N = 112) of small- and medium-sized organizations across different industries in Ontario, Canada. The regression analysis confirmed a positive statistically significant relationship between study variables and BI. Overall, the model significantly predicted BI, F (7, 100) = 76.097, p < .001, R2 = .842. Performance expectancy (β = .356, p < .001), habit (β = .269, p < .001), and social influence (β = .258, p < .001) were significant predictors of BI at the .001 level whereas effort expectancy (β = .187, p < .01), facilitating conditions (β = .114, p < .01), hedonic motivation (β = .107, p < .01), and price value (β =.105, p < .01), were significant predictors at the .005 level. Using study results, chief information officers may be able to develop improved strategies to facilitate IT consumerization. Implications for positive social change include more flexibility and convenience for employees in managing their work and social lives.

The shift in the landscape of healthcare services from inpatient care to outpatient care prompts healthcare leaders to re-evaluate their strategies to boost declining revenue. Telehealth offers potential for increasing efficiency and access to care, and the acceptance of its modal quality is essential for its diffusion and adoption. The purpose of this single case study was to explore strategies that healthcare providers used to implement telehealth to increase profitability. The conceptual framework was the technology acceptance model. Data were collected through semistructured interviews and review of organizational documents. The research population comprised 4 healthcare leaders in 1 organization in the midwestern region of the United States who had successfully implemented telehealth. Three main themes emerged from coding of phrases, word frequency searches, and data analysis: implementation strategies, obstacles in implementation, and user acceptance of telehealth. The findings from this study may contribute to the implementation of telehealth business practices by providing healthcare leaders with strategies to successfully implement telehealth to improve profitability. These strategies could help to provide suitable healthcare at lower costs and improve quality of life for patients.

In 2015, a sharp decline in the sustainability of small agrarian businesses in Sierra Leone resulted in the gross domestic product declining by 41%. The purpose of this multiple case study was to explore the innovation strategies owners of small agrarian businesses use to sustain their businesses for longer than 5 years. The disruptive innovation theory was the conceptual framework for this study. The participants were 16 owners of small agrarian businesses from Makeni, Kenema, Bo, and Freetown districts in Sierra Leone who have sustained their businesses for longer than 5 years. Data were collected using semistructured interviews, personal notes, and a review of the ministry of agriculture documents. Member checking and methodological triangulation increased the validity and reliability of the study findings. Content and thematic data analysis using Yin's 5-step process provided the basis for identifying the findings. Data analysis resulted in the emergence of 5 themes: financial support, leadership, technology, enhanced competency, and organizational culture. The implications for positive social change include the potential to create employment opportunities for youths in the communities by enabling agrarian businesses in Sierra Leone to succeed and expand using innovation strategies.