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- All Subjects: Computer Science
- All Subjects: Communication
- Genre: Doctoral Dissertation
- Creators: Arizona State University
Description
As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
The United States is facing an emerging principal shortage. This study examines an intervention to deliver professional development for assistant principals on their way to becoming principals. The intervention intended to boost their sense of efficacy as if they were principals while creating a supportive community of professionals for ongoing professional learning. The community was designed much like a professional learning community (PLC) with the intent of developing into a community of practice (CoP). The participants were all elementary school assistant principals in a Title I district in a large metropolitan area. The researcher interviewed an expert set of school administrators consisting of superintendents and consultants (and others who have knowledge of what a good principal ought to be) about what characteristics and skills were left wanting in principal applicants. The data from these interviews provided the discussion topics for the intervention. The assistant principals met regularly over the course of a semester and discussed the topics provided by the expert set of school administrators. Participant interaction within the sessions followed conversation protocols. The researcher was also a participant in the group and served as the coordinator. Each session was recorded and transcribed. The researcher used a mixed methods approach to analyze the intervention. Participants were surveyed to measure their efficacy before and after the intervention. The session transcripts were analyzed using open and axial coding. Data showed no statistically significant change in the participants' sense of efficacy. Data also showed the participants became a coalescing community of practice.
ContributorsRichman, Bryan (Author) / Puckett, Kathleen (Thesis advisor) / Smith, Jeffery (Committee member) / Foulger, Teresa (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation explores the rhetorical significance of persecution claims produced by demonstrably powerful publics in contemporary American culture. This ideological criticism is driven by several related research questions. First, how do members of apparently powerful groups (men, whites, and Christians) come to see themselves as somehow unjustly marginalized, persecuted, or powerless? Second, how are these discourses related to the public sphere and counterpublicity? I argue that, despite startling similarities, these texts studied here are best understood not as counterpublicity but as a strategy of containment available to hegemonic publics. Because these rhetorics of persecution often seek to forestall movements toward pluralism and restorative justice, the analysis forwarded in this dissertation offers important contributions to ongoing theoretical discussions in the fields of public sphere theory and critical cultural theory and practical advice for progressive political activism and critical pedagogy.
ContributorsDuerringer, Christopher (Author) / Brouwer, Daniel (Thesis advisor) / Carlson, Cheree (Committee member) / McDonald, Kelly (Committee member) / Arizona State University (Publisher)
Created2011
Deviant bodies resisting online: examining the intersecting realities of women of color in Xbox Live
Description
Employing qualitative methods and drawing from an intersectional framework which focuses on the multiple identities we all embody, this dissertation focuses on oppressions and resistance strategies employed by women of color in Xbox live, an online gaming community. Ethnographic observations and narrative interviewing reveal that women of color, as deviants within the space, face intersecting oppressions in gaming as in life outside the gaming world. They are linguistically profiled within the space based off of how they sound. They have responded with various strategies to combat the discrimination they experience. Some segregate themselves from the larger gaming population and many refuse to purchase games that depict women in a hyper-sexualized manner or that present people of color stereotypically. For others, the solution is to "sit-in" on games and disrupt game flow by 'player-killing' or engage in other 'griefing' activities. I analyze this behavior in the context of Black feminist consciousness and resistance and uncover that these methods are similar to women who employ resistance strategies for survival within the real world.
ContributorsGray, Kishonna (Author) / Anderson, Lisa M. (Thesis advisor) / Cheong, Pauline (Committee member) / Lim, Merlyna (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Service based software (SBS) systems are software systems consisting of services based on the service oriented architecture (SOA). Each service in SBS systems provides partial functionalities and collaborates with other services as workflows to provide the functionalities required by the systems. These services may be developed and/or owned by different entities and physically distributed across the Internet. Compared with traditional software system components which are usually specifically designed for the target systems and bound tightly, the interfaces of services and their communication protocols are standardized, which allow SBS systems to support late binding, provide better interoperability, better flexibility in dynamic business logics, and higher fault tolerance. The development process of SBS systems can be divided to three major phases: 1) SBS specification, 2) service discovery and matching, and 3) service composition and workflow execution. This dissertation focuses on the second phase, and presents a privacy preserving service discovery and ranking approach for multiple user QoS requirements. This approach helps service providers to register services and service users to search services through public, but untrusted service directories with the protection of their privacy against the service directories. The service directories can match the registered services with service requests, but do not learn any information about them. Our approach also enforces access control on services during the matching process, which prevents unauthorized users from discovering services. After the service directories match a set of services that satisfy the service users' functionality requirements, the service discovery approach presented in this dissertation further considers service users' QoS requirements in two steps. First, this approach optimizes services' QoS by making tradeoff among various QoS aspects with users' QoS requirements and preferences. Second, this approach ranks services based on how well they satisfy users' QoS requirements to help service users select the most suitable service to develop their SBSs.
ContributorsYin, Yin (Author) / Yau, Stephen S. (Thesis advisor) / Candan, Kasim (Committee member) / Dasgupta, Partha (Committee member) / Santanam, Raghu (Committee member) / Arizona State University (Publisher)
Created2011
Description
Dangerous drinking on college campuses is a significant public health issue. Over the last decade, the National Institute on Alcohol Abuse and Alcoholism and the U.S. Department of Health and Human Services have called on universities, community leaders, policymakers, parents and students to work together to develop effective, research based alcohol prevention and/or intervention programs. Despite such calls, parent-based prevention programs are relatively rare on college campuses, and there is a paucity of research on the ways in which parents influence their emerging adult children's drinking behaviors. The present project is designed to help address this need. Grounded in social cognitive theory, this exploratory study focuses on alcohol communication and poses numerous questions regarding the alcohol messages exchanged between college students and their parents, as well as how such messages associate with college students' dangerous drinking. Undergraduate students ages 18 to 25 who were enrolled in communication classes were recruited for the study and asked to recruit a parent. The sample included 198 students and 188 parents, all of whom completed an online survey. Results indicated the majority of college students have had alcohol conversations with a parent since the student graduated from high school. Parents viewed such conversations as significantly more open, direct, and ongoing than did students; though both generally agreed on the content of their alcohol communication, reporting an emphasis on the negative aspects of drinking, particularly the dangers of drinking and driving and the academic consequences of too much partying. Frequent discussions of drinking risks had significant, positive associations with students' dangerous drinking, whereas parents' reports of discussing rules about alcohol had a significant negative association with students' alcohol consumption. There were strong significant associations between the types alcohol topics discussed and students' perception that their parents approved of their drinking, as well as parents' actual approval. Perceived approval had a significant, positive association with students' dangerous drinking; however, actual parental approval was not a significant predictor of students' drinking outcomes. Parents' alcohol consumption had a significant positive association with students' alcohol consumption. Implications for parents, public health practitioners, and future research are discussed.
ContributorsMenegatos, Lisa Marie (Author) / Floyd, Kory (Thesis advisor) / Lederman, Linda C. (Thesis advisor) / Valiente, Carlos (Committee member) / Arizona State University (Publisher)
Created2011
Description
As digital technology promises immediacy and interactivity in communication, sight and sound in motion graphics has expanded the range of design possibilities in advertising, social networking, and telecommunication beyond the visual realm. The experience of seeing has been greatly enriched by sound as visual solutions become dynamic and multi-dimensional. The ability to record and transfer sight and sound with new media has granted the designer more control in manipulating a viewer's experience of time and space. This control allows time-based form to become the foundation that establishes many interactive, multisensory and interdisciplinary applications. Is conventional design theory for print media adequate to effectively approach time-based form? If not, what is the core element that is required to balance the static and dynamic aspects of time in new media? Should time-related theories and methodologies from other disciplines be adopted into our design principles? If so, how would this knowledge be integrated? How can this experience in time be effectively transferred to paper? Unless the role of the time dimension in sight is operationally deconstructed and retained with sound, it is very challenging to control the design in this fugitive form. Time activation refers to how time and the perception of time can be manipulated for design and communication purposes. Sound, as a shortcut to the active time design element, not only encapsulates the structure of its "invisible" time-based form, but also makes changes in time conspicuously measurable and comparable. Two experiments reflect the influence of sound on imagery, a slideshow and video, as well as how the dynamics in time are represented across all design media. A cyclical time-based model is established to reconnect the conventional design principles learned in print media with time-based media. This knowledge helps expand static images to motion and encapsulate motion in stasis. The findings provide creative methods for approaching visualization, interactivity, and design education.
ContributorsCheung, Hoi Yan Patrick (Author) / Giard, Jacques (Thesis advisor) / Sanft, Alfred C (Committee member) / Aisling, Kelliher (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the rapid growth of mobile computing and sensor technology, it is now possible to access data from a variety of sources. A big challenge lies in linking sensor based data with social and cognitive variables in humans in real world context. This dissertation explores the relationship between creativity in teamwork, and team members' movement and face-to-face interaction strength in the wild. Using sociometric badges (wearable sensors), electronic Experience Sampling Methods (ESM), the KEYS team creativity assessment instrument, and qualitative methods, three research studies were conducted in academic and industry R&D; labs. Sociometric badges captured movement of team members and face-to-face interaction between team members. KEYS scale was implemented using ESM for self-rated creativity and expert-coded creativity assessment. Activities (movement and face-to-face interaction) and creativity of one five member and two seven member teams were tracked for twenty five days, eleven days, and fifteen days respectively. Day wise values of movement and face-to-face interaction for participants were mean split categorized as creative and non-creative using self- rated creativity measure and expert-coded creativity measure. Paired-samples t-tests [t(36) = 3.132, p < 0.005; t(23) = 6.49 , p < 0.001] confirmed that average daily movement energy during creative days (M = 1.31, SD = 0.04; M = 1.37, SD = 0.07) was significantly greater than the average daily movement of non-creative days (M = 1.29, SD = 0.03; M = 1.24, SD = 0.09). The eta squared statistic (0.21; 0.36) indicated a large effect size. A paired-samples t-test also confirmed that face-to-face interaction tie strength of team members during creative days (M = 2.69, SD = 4.01) is significantly greater [t(41) = 2.36, p < 0.01] than the average face-to-face interaction tie strength of team members for non-creative days (M = 0.9, SD = 2.1). The eta squared statistic (0.11) indicated a large effect size. The combined approach of principal component analysis (PCA) and linear discriminant analysis (LDA) conducted on movement and face-to-face interaction data predicted creativity with 87.5% and 91% accuracy respectively. This work advances creativity research and provides a foundation for sensor based real-time creativity support tools for teams.
ContributorsTripathi, Priyamvada (Author) / Burleson, Winslow (Thesis advisor) / Liu, Huan (Committee member) / VanLehn, Kurt (Committee member) / Pentland, Alex (Committee member) / Arizona State University (Publisher)
Created2011