Knowledge assisted data management and retrieval in multimedia database systems

With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular, the focus of this study is to facilitate the high-level video indexing by proposing a multimodal event mining framework associated with temporal knowledge discovery approaches. With respect to the perception subjectivity issue, advanced techniques are proposed to support users' interaction and to effectively model users' perception from the feedback at both the image-level and object-level.

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Development and implementation of a tool to assess the micro/macro elements in an organization

This study examines the congruency of planning between organizational structure and process, through an evaluation and planning model known as the Micro/Macro Dynamic Planning Grid. The model compares day-to-day planning within an organization to planning imposed by organizational administration and accrediting agencies. A survey instrument was developed to assess the micro and macro sociological analysis elements utilized by an organization.^ The Micro/Macro Dynamic Planning Grid consists of four quadrants. Each quadrant contains characteristics that reflect the interaction between the micro and macro elements of planning, objectives and goals within an organization. The Over Macro/Over Micro, Quadrant 1, contains attributes that reflect a tremendous amount of action and ongoing adjustments, typical of an organization undergoing significant changes in either leadership, program and/or structure. Over Macro/Under Micro, Quadrant 2, reflects planning characteristics found in large, bureaucratic systems with little regard given to the workings of their component parts. Under Macro/Under Micro, Quadrant 3, reflects the uncooperative, uncoordinated organization, one that contains a multiplicity of viewpoints, language, objectives and goals. Under Macro/Under Micro, Quadrant 4 represents the worst case scenario for any organization. The attributes of this quadrant are very reactive, chaotic, non-productive and redundant.^ There were three phases to the study: development of the initial instrument, pilot testing the initial instrument and item revision, and administration and assessment of the refined instrument. The survey instrument was found to be valid and reliable for the purposes and audiences herein described.^ In order to expand the applicability of the instrument to other organizational settings, the survey was administered to three professional colleges within a university.^ The first three specific research questions collectively answered, in the affirmative, the basic research question: Can the Micro/Macro Dynamic Planning Grid be applied to an organization through an organizational development tool? The first specific question: Can an instrument be constructed that applies the Micro/Macro Dynamic Planning Grid? The second specific research question: Is the constructed instrument valid and reliable? The third specific research question: Does an instrument that applies the Micro/Macro Dynamic Planning Grid assess congruency of micro and macro planning, goals and objectives within an organization? The fourth specific research question: What are the differences in the responses based on roles and responsibilities within an organization? involved statistical analysis of the response data and comparisons obtained with the demographic data. (Abstract shortened by UMI.) ^

A study of intrablack conflict and work attitudes within an African American organization

The present research evidences a field setting studying attitudinal and behavioral results of five Black group contacts. The research was designed, in part, to determine the demographic, cultural, social, and psychological factors associated with intrablack perceptions of conflict and work attitudes in an African American organization. Two organizational groups, African Americans and Caribbean/West Indians totaling 112 participants were studied. The objective of the research was to gain information about attitudinal levels perceived by each of the two groups. Each group rated the other group on items dealing with conflict and work attitudes. One-way analysis of variances (ANOVAs) were employed to test the overall differences on scale means among the groups. The findings in this study buttress some of the major themes in the impressionistic literature on cultural/multicultural diversity in organizations and Caribbean/West Indian literature. The data are reported and examined, and theoretical implications are discussed. ^

Community college adjunct policies and practices: Incumbents' perspectives and congruence with learning organization tenets

The primary research question was: What is the nature and degree of alignment between the tenets of learning organizations and the policies and practices of a community college concerning adjunct instructors? I investigated the employment experiences of 8 adjunct instructors at a large community college in the Southeastern U.S. to (a) describe and explain the perspectives of the adjuncts, (b) describe and explain my own adjunct employment experience at the same college, (c) determine how the adjunct policies and practices collectively encountered were congruent with or at variance with the tenets of learning organizations, and (d) to use this framework to support recommendations that may help the college achieve more favorable alignment with these tenets. ^ Data on perceived adjunct policies and practices were reduced into 11 categories and, using matrices, were compared with 5 major categories of learning organization tenets. The 5 categories of tenets were: (a) inputs, (b) information flow/communication, (c) employee inclusion/value, (d) teamwork, and (e) facilitation of change. The 11 categories of the college's policies and practices were (a) becoming an adjunct, (b) full-time employment aspirations, (c) salary, (d) benefits, (e) job security and predictability, (f) job satisfaction, (g) respect, (h) support services, (i) professional development, (j) institutional inclusion, and (k) future role of adjuncts. The reflective journal component relied on a 5-year (1995–2000) personal and professional journal maintained by me during employment with the same college as the participants. ^ Findings indicate that the college's adjunct policies and practices were most incongruent with 25 of the 70 learning organization tenets. These incongruencies spanned the 5 categories, although most occurred in the Employee/Inclusion/Value category. Adjunct instructors wanted inclusion, respect, value, trust, and empowerment in decision making processes that affect adjunct policies and practices of the college, but did not perceive this to be a part of the present situation. ^

State legislative term limits and the law of unintended consequences: An examination of member behavior, power structures, and legislative organization in Florida

The implementation of term limits on state legislators has provided a wealth of data for study. Florida, the second largest state in the Union with term limits, has not been comprehensively studied. This research examines the effects of term limits on electoral competition, member composition, legislator career paths, legislative leadership, and intra- and inter-governmental influences on Florida's legislature. This study looks at the Florida legislature from 1992 when term limits were enacted through 2004, three electoral cycles in which term limits have been in effect. This study uses both quantitative and qualitative data where appropriate. Electoral data is used to assess electoral and demographic effects, as well as member career trajectories. Interview data with current and former legislators, lobbyists, and executive branch officials is used to analyze both changes in legislative organization and intra- and inter-governmental influences on the legislative process. Term limits has only created greater competition when a legislative seat opens and has actually created a greater advantage for incumbents. Women and minorities have only made minimal gains in winning seats post-term limits. Newly elected legislators are not political novices with a vast majority having previous elective experience. Leadership is more centralized under term limits and the Senate has gained an advantage over the more inexperienced House. Lastly, the influence of staff, lobbyists, and most importantly, the governor has greatly increased under term limits. This research finds that term limits have not produced the consequences that proponents had envisioned.^

Knowledge discovery using multiple sources of biological data

The primary aim of this dissertation is to develop data mining tools for knowledge discovery in biomedical data when multiple (homogeneous or heterogeneous) sources of data are available. The central hypothesis is that, when information from multiple sources of data are used appropriately and effectively, knowledge discovery can be better achieved than what is possible from only a single source. ^ Recent advances in high-throughput technology have enabled biomedical researchers to generate large volumes of diverse types of data on a genome-wide scale. These data include DNA sequences, gene expression measurements, and much more; they provide the motivation for building analysis tools to elucidate the modular organization of the cell. The challenges include efficiently and accurately extracting information from the multiple data sources; representing the information effectively, developing analytical tools, and interpreting the results in the context of the domain. ^ The first part considers the application of feature-level integration to design classifiers that discriminate between soil types. The machine learning tools, SVM and KNN, were used to successfully distinguish between several soil samples. ^ The second part considers clustering using multiple heterogeneous data sources. The resulting Multi-Source Clustering (MSC) algorithm was shown to have a better performance than clustering methods that use only a single data source or a simple feature-level integration of heterogeneous data sources. ^ The third part proposes a new approach to effectively incorporate incomplete data into clustering analysis. Adapted from K-means algorithm, the Generalized Constrained Clustering (GCC) algorithm makes use of incomplete data in the form of constraints to perform exploratory analysis. Novel approaches for extracting constraints were proposed. For sufficiently large constraint sets, the GCC algorithm outperformed the MSC algorithm. ^ The last part considers the problem of providing a theme-specific environment for mining multi-source biomedical data. The database called PlasmoTFBM, focusing on gene regulation of Plasmodium falciparum, contains diverse information and has a simple interface to allow biologists to explore the data. It provided a framework for comparing different analytical tools for predicting regulatory elements and for designing useful data mining tools. ^ The conclusion is that the experiments reported in this dissertation strongly support the central hypothesis.^

The role of the health care organization in supporting nurses in the delivery of culturally competent care

This study was designed to explore ways in which health care organizations (HCOs) can support nurses in their delivery of culturally competent care. While cultural competence has become a priority for the federal government as well as the major health professional organizations, its integration into care delivery has not yet been realized. Health professionals cite a lack of educational preparation, time, and organizational resources as barriers. Most experts in the field agree that the cultural and linguistic needs of ethnic minorities pose challenges that individual care providers are unable to manage without the support of the health care organizations within which they practice. While several studies have identified implications for HCOs, there is a paucity of research on their role in this aspect of care delivery. Using a qualitative design with a case study approach, data collection included face-to-face interviews with 23 registered nurses, document analysis, and reports of critical incidents. The site chosen was a large health care system in South Florida that serves a culturally diverse population. Major findings from the study included language barriers, lack of training, difficulty with cultural differences, lack of organizational support, and reliance on culturally diverse staff members. Most nurses thought the ethnic mix was adequate, but rated other supports such as language services, training, and patient education materials as inadequate. Some of the recommendations for organizational performance were to provide the expectations and support for culturally competent care. Implications and recommendations for practice include nurses using trained interpreters instead of relying on coworkers or trying to "wing it", pursuing training, and advocating for organizational supports for culturally competent care. Implications and recommendations for theory included a blended model that combines both models in the conceptual framework. Recommendations for future research were for studies on the impact of language bathers on care delivery, develop and test a quantitative instrument, and to incorporate Gilbert's model into nursing research.

An investigation of the strategies used by organizations participating in the welfare-to-work network program and correlation of the strategies with the retention data to determine best practices for job retention among former welfare recipients

This ex post facto study (N = 209) examined the relationships between employer job strategies and job retention among organizations participating in Florida welfare-to-work network programs and associated the strategies with job retention data to determine best practices. ^ An internet-based self-report survey battery was administered to a heterogeneous sampling of organizations participating in the Florida welfare-to-work network program. Hypotheses were tested through correlational and hierarchical regression analytic procedures. The partial correlation results linked each of the job retention strategies to job retention. Wages, benefits, training and supervision, communication, job growth, work/life balance, fairness and respect were all significantly related to job retention. Hierarchical regression results indicated that the training and supervision variable was the best predictor of job retention in the regression equation. ^ The size of the organization was also a significant predictor of job retention. Large organizations reported higher job retention rates than small organizations. There was no statistical difference between the types of organizations (profit-making and non-profit) and job retention. The standardized betas ranged from to .26 to .41 in the regression equation. Twenty percent of the variance in job retention was explained by the combination of demographic and job retention strategy predictors, supporting the theoretical, empirical, and practical relevance of understanding the association between employer job strategies and job retention outcomes. Implications for adult education and human resource development theory, research, and practice are highlighted as possible strategic leverage points for creating conditions that facilitate the development of job strategies as a means for improving former welfare workers’ job retention.^

Variables affecting the success of older applicants for employment in a public organization: An age -cued cognitive model of administrative decision-making

The dissertation takes a multivariate approach to answer the question of how applicant age, after controlling for other variables, affects employment success in a public organization. In addition to applicant age, there are five other categories of variables examined: organization/applicant variables describing the relationship of the applicant to the organization; organization/position variables describing the target position as it relates to the organization; episodic variables such as applicant age relative to the ages of competing applicants; economic variables relating to the salary needs of older applicants; and cognitive variables that may affect the decision maker's evaluation of the applicant. ^ An exploratory phase of research employs archival data from approximately 500 decisions made in the past three years to hire or promote applicants for positions in one public health administration organization. A logit regression model is employed to examine the probability that the variables modify the effect of applicant age on employment success. A confirmatory phase of the dissertation is a controlled experiment in which hiring decision makers from the same public organization perform a simulated hiring decision exercise to evaluate hypothetical applicants of similar qualifications but of different ages. The responses of the decision makers to a series of bipolar adjective scales add support to the cognitive component of the theoretical model of the hiring decision. A final section contains information gathered from interviews with key informants. ^ Applicant age has tended to have a curvilinear relationship with employment success. For some positions, the mean age of the applicants most likely to succeed varies with the values of the five groups of moderating variables. The research contributes not only to the practice of public personnel administration, but is useful in examining larger public policy issues associated with an aging workforce. ^

Phylogenetic analysis of within -host serially-sampled viral data

The primary goal of this dissertation is the study of patterns of viral evolution inferred from serially-sampled sequence data, i.e., sequence data obtained from strains isolated at consecutive time points from a single patient or host. RNA viral populations have an extremely high genetic variability, largely due to their astronomical population sizes within host systems, high replication rate, and short generation time. It is this aspect of their evolution that demands special attention and a different approach when studying the evolutionary relationships of serially-sampled sequence data. New methods that analyze serially-sampled data were developed shortly after a groundbreaking HIV-1 study of several patients from which viruses were isolated at recurring intervals over a period of 10 or more years. These methods assume a tree-like evolutionary model, while many RNA viruses have the capacity to exchange genetic material with one another using a process called recombination. ^ A genealogy involving recombination is best described by a network structure. A more general approach was implemented in a new computational tool, Sliding MinPD, one that is mindful of the sampling times of the input sequences and that reconstructs the viral evolutionary relationships in the form of a network structure with implicit representations of recombination events. The underlying network organization reveals unique patterns of viral evolution and could help explain the emergence of disease-associated mutants and drug-resistant strains, with implications for patient prognosis and treatment strategies. In order to comprehensively test the developed methods and to carry out comparison studies with other methods, synthetic data sets are critical. Therefore, appropriate sequence generators were also developed to simulate the evolution of serially-sampled recombinant viruses, new and more through evaluation criteria for recombination detection methods were established, and three major comparison studies were performed. The newly developed tools were also applied to "real" HIV-1 sequence data and it was shown that the results represented within an evolutionary network structure can be interpreted in biologically meaningful ways. ^

The World Trade Organization, competition policy, and developing countries: A cross -country examination of domestic policy actions

This dissertation addresses the following research question: in a particular policy area, why do countries that display unanimity in their international policy behavior diverge from each other in their domestic policy actions? I address this question in the context of the divergent domestic competition policy actions undertaken by developing countries during the period 1996-2007, after these countries had quite conspicuously displayed near-unanimity in opposing this policy measure at the World Trade Organization (WTO). This divergence is puzzling because (a) it does not align with their near-unanimous behavior at the WTO over competition policy and (b) it is at variance with the objectives of their international opposition to this policy at the WTO. Using an interdisciplinary approach, this dissertation examines the factors responsible for this divergence in the domestic competition policy actions of developing countries. ^ The theoretical structure employed in this study is the classic second-image-reversed framework in international relations theory that focuses on the domestic developments in various countries following an international development. Methodologically, I employ both quantitative and qualitative methods of analysis to ascertain the nature of the relationship between the dependent variable and the eight explanatory variables that were identified from existing literature. The data on some of the key variables used in this dissertation was uniquely created over a multi-year period through extensive online research and represents the most comprehensive and updated dataset currently available. ^ The quantitative results obtained from logistic regression using data on 131 countries point toward the significant role played by international organizations in engineering change in this policy area in developing countries. The qualitative analysis consisting of three country case studies illuminate the channels of influence of the explanatory variables and highlight the role of domestic-level factors in these three carefully selected countries. After integrating the findings from the quantitative and qualitative analyses, I conclude that a mix of international- and domestic-level variables explains the divergence in domestic competition policy actions among developing countries. My findings also confirm the argument of the second-image-reversed framework that, given an international development or situation, the policy choices that states make can differ from each other and are mediated by domestic-level factors. ^

Developing a Data-Driven Classification of South Florida Plant Communities

A comprehensive, broadly accepted vegetation classification is important for ecosystem management, particularly for planning and monitoring. South Florida vegetation classification systems that are currently in use were largely arrived at subjectively and intuitively with the involvement of experienced botanical observers and ecologists, but with little support in terms of quantitative field data. The need to develop a field data-driven classification of South Florida vegetation that builds on the ecological organization has been recognized by the National Park Service and vegetation practitioners in the region. The present work, funded by the National Park Service Inventory and Monitoring Program - South Florida/Caribbean Network (SFCN), covers the first stage of a larger project whose goal is to apply extant vegetation data to test, and revise as necessary, an existing, widely used classification (Rutchey et al. 2006). The objectives of the first phase of the project were (1) to identify useful existing datasets, (2) to collect these data and compile them into a geodatabase, (3) to conduct an initial classification analysis of marsh sites, and (4) to design a strategy for augmenting existing information from poorly represented landscapes in order to develop a more comprehensive south Florida classification.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.