Matching Items (254)
Description
Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU ISA. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform. This thesis addresses the overhead incurred in the JNI due to reflection and serialization when objects are accessed on android based mobile devices. It provides techniques to reduce this overhead. It also provides an API to access objects through its reference through pinning its memory location. The Android emulator was used to evaluate the performance of these techniques and we observed that there was 5 - 10 % performance gain in the new Java Native Interface.
ContributorsChandrian, Preetham (Author) / Lee, Yann-Hang (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the increasing focus on developing environmentally benign electronic packages, lead-free solder alloys have received a great deal of attention. Mishandling of packages, during manufacture, assembly, or by the user may cause failure of solder joint. A fundamental understanding of the behavior of lead-free solders under mechanical shock conditions is lacking. Reliable experimental and numerical analysis of lead-free solder joints in the intermediate strain rate regime need to be investigated. This dissertation mainly focuses on exploring the mechanical shock behavior of lead-free tin-rich solder alloys via multiscale modeling and numerical simulations. First, the macroscopic stress/strain behaviors of three bulk lead-free tin-rich solders were tested over a range of strain rates from 0.001/s to 30/s. Finite element analysis was conducted to determine appropriate specimen geometry that could reach a homogeneous stress/strain field and a relatively high strain rate. A novel self-consistent true stress correction method is developed to compensate the inaccuracy caused by the triaxial stress state at the post-necking stage. Then the material property of micron-scale intermetallic was examined by micro-compression test. The accuracy of this measure is systematically validated by finite element analysis, and empirical adjustments are provided. Moreover, the interfacial property of the solder/intermetallic interface is investigated, and a continuum traction-separation law of this interface is developed from an atomistic-based cohesive element method. The macroscopic stress/strain relation and microstructural properties are combined together to form a multiscale material behavior via a stochastic approach for both solder and intermetallic. As a result, solder is modeled by porous plasticity with random voids, and intermetallic is characterized as brittle material with random vulnerable region. Thereafter, the porous plasticity fracture of the solders and the brittle fracture of the intermetallics are coupled together in one finite element model. Finally, this study yields a multiscale model to understand and predict the mechanical shock behavior of lead-free tin-rich solder joints. Different fracture patterns are observed for various strain rates and/or intermetallic thicknesses. The predictions have a good agreement with the theory and experiments.
ContributorsFei, Huiyang (Author) / Jiang, Hanqing (Thesis advisor) / Chawla, Nikhilesh (Thesis advisor) / Tasooji, Amaneh (Committee member) / Mobasher, Barzin (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2011
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
The Kasturba Gandhi Balika Vidyalaya (KGBV) policy scheme launched in 2004 by the Ministry of Human Resource Development, the Government of India, aims to provide secondary level education (grade 6-8) for girls residing predominantly in minority communities, the Scheduled Caste (SC), the Scheduled Tribe (ST), and the Other Backward Caste (OBC). Since its launch, the Government of India established 2,578 KGBV schools in 27 states and union territories (UTs). The present study examines the new policy and its implementation at three KGBV schools located in rural villages of Uttar Pradesh (UP), India. The purpose was to analyze the Government of India's approach to increasing education opportunity and participation for educationally disadvantaged girls using the empowerment framework developed by Deepa Narayan. Observations at three schools, interviews with teachers and staff members of the implementation agency (i.e., Mahila Samakhya (MS)), and surveys administered to 139 teachers were conducted over a four month period in 2009. Adopting creative teaching approaches and learning activities, MS creates safe learning community which is appropriate for the rural girls. MS gives special attention to nurturing the girls' potential and empowering them inside and outside the school environment through social discussion, parental involvement, rigid discipline and structure, health and hygiene education, and physical and mental training. Interviews with the state program director and coordinators identified some conflicts within government policy schemes such as the Teacher-pupil ratios guidelines as a part of the programs for the universalization of elementary education. Major challenges include a high turnover rate of teachers, a lack of female teachers, a lack of provision after Class 8, and inadequate budget for medical treatment. Recommendations include promoting active involvement of male members in the process of girls' empowerment, making MS approaches of girls' education in rural settings standardized for wider dissemination, and developing flexible and strong partnership among local agencies and government organizations for effective service delivery.
ContributorsWatanabe, Miku (Author) / Fischman, Gustavo (Thesis advisor) / Wiley, Terrence (Committee member) / Mccarty, Teresa (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
Most existing approaches to complex event processing over streaming data rely on the assumption that the matches to the queries are rare and that the goal of the system is to identify these few matches within the incoming deluge of data. In many applications, such as stock market analysis and user credit card purchase pattern monitoring, however the matches to the user queries are in fact plentiful and the system has to efficiently sift through these many matches to locate only the few most preferable matches. In this work, we propose a complex pattern ranking (CPR) framework for specifying top-k pattern queries over streaming data, present new algorithms to support top-k pattern queries in data streaming environments, and verify the effectiveness and efficiency of the proposed algorithms. The developed algorithms identify top-k matching results satisfying both patterns as well as additional criteria. To support real-time processing of the data streams, instead of computing top-k results from scratch for each time window, we maintain top-k results dynamically as new events come and old ones expire. We also develop new top-k join execution strategies that are able to adapt to the changing situations (e.g., sorted and random access costs, join rates) without having to assume a priori presence of data statistics. Experiments show significant improvements over existing approaches.
ContributorsWang, Xinxin (Author) / Candan, K. Selcuk (Thesis advisor) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
Description
In this dissertation I present data gathered from an eleven-month qualitative research study with adolescents living and working on the streets of Lima, Peru. Through the pairing of photovoice with participant observations, this work incorporates distinctive methodological and theoretical viewpoints in order to complicate prevailing understandings of street life. In this dissertation, I examine the identities that children and adolescents on the street develop in context, and the ways in which photography can be a useful tool in understanding identity development among this population. Through a framework integrating theories of identity and identity performance with spatial theories, I outline how identity development among children and adolescents living on the street is directly connected to their relationships with the urban landscape and the outreach organizations that serve them. The organizations and institutions that surround children on the street shape who they are, how they are perceived by society, and how they view and understand themselves in context. It is through the interaction with aid organizations and the urban landscape that a street identity is learned and developed. Furthermore, as organizations, children and adolescents come together within the context of the city, a unique street space is created. I argue that identity and agency are directly tied to this space. I also present the street as a thirdspace of possibility, where children and adolescents are able to act out various aspects of the self that they would be unable to pursue otherwise. Weaved throughout this dissertation are non-traditional writing forms including narrative and critical personal narrative addressing my own experiences conducting this research, my impact on the research context, and how I understand the data gathered.
ContributorsJoanou, Jamie Patrice (Author) / Swadener, Beth B. (Thesis advisor) / Margolis, Eric (Committee member) / Arzubiaga, Angela (Committee member) / Fischman, Gustavo (Committee member) / Arizona State University (Publisher)
Created2011
Description
The Civil Rights Project estimates that Black girls are among the least likely to graduate from high school. More specifically, only about half, or 56%, of freshman Black girls graduate with their class four years later. Beyond the statistics little is known about Black girls who drop out, why they leave school and what happens to them once they are gone. This study is a grounded theory analysis of the stories eight adult Black women told about dropping out of high school with a particular focus on how dropping out affected their lives as workers, mothers and returners to education. There is one conclusion about dropping out and another about Black female identity. First, the women in my study were adolescents during the 1980s, experienced life at the intersection of Blackness, womaness, and poverty and lived in the harsh conditions of a Black American hyperghetto. Using a synthesis between intersectionality and hyperghettoization I found that the women were so determined to improve their economic and personal conditions that they took on occupations that seemed to promise freedom, wealth and safety. Because they were so focused on their new lives, their school attendance suffered as a consequence. In the second conclusion I argued that Black women draw their insights about Black female identity from two competing sources. The two sources are their lived experience and popular controlling images of Black female identity.
ContributorsGriffin, Erica Nicole (Author) / Powers, Jeanne (Thesis advisor) / Fischman, Gustavo (Committee member) / Margolis, Eric (Committee member) / Arizona State University (Publisher)
Created2011
Description
Data-driven applications are becoming increasingly complex with support for processing events and data streams in a loosely-coupled distributed environment, providing integrated access to heterogeneous data sources such as relational databases and XML documents. This dissertation explores the use of materialized views over structured heterogeneous data sources to support multiple query optimization in a distributed event stream processing framework that supports such applications involving various query expressions for detecting events, monitoring conditions, handling data streams, and querying data. Materialized views store the results of the computed view so that subsequent access to the view retrieves the materialized results, avoiding the cost of recomputing the entire view from base data sources. Using a service-based metadata repository that provides metadata level access to the various language components in the system, a heuristics-based algorithm detects the common subexpressions from the queries represented in a mixed multigraph model over relational and structured XML data sources. These common subexpressions can be relational, XML or a hybrid join over the heterogeneous data sources. This research examines the challenges in the definition and materialization of views when the heterogeneous data sources are retained in their native format, instead of converting the data to a common model. LINQ serves as the materialized view definition language for creating the view definitions. An algorithm is introduced that uses LINQ to create a data structure for the persistence of these hybrid views. Any changes to base data sources used to materialize views are captured and mapped to a delta structure. The deltas are then streamed within the framework for use in the incremental update of the materialized view. Algorithms are presented that use the magic sets query optimization approach to both efficiently materialize the views and to propagate the relevant changes to the views for incremental maintenance. Using representative scenarios over structured heterogeneous data sources, an evaluation of the framework demonstrates an improvement in performance. Thus, defining the LINQ-based materialized views over heterogeneous structured data sources using the detected common subexpressions and incrementally maintaining the views by using magic sets enhances the efficiency of the distributed event stream processing environment.
ContributorsChaudhari, Mahesh Balkrishna (Author) / Dietrich, Suzanne W (Thesis advisor) / Urban, Susan D (Committee member) / Davulcu, Hasan (Committee member) / Chen, Yi (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT Early childhood education (ECE) teacher professional development refers to the various modalities of providing new and or additional content knowledge to the teachers who work with children birth to five. The purpose of this study was to examine the effectiveness of an Arizona United Way-administered intervention project designed to provide focused professional development activities to 15 ECE teachers at seven high-need, center-based early care and education settings. Specifically, this study determined if these interventions influenced the teachers to undertake formative career path changes such as college coursework. In addition, the study also sought to understand the views, beliefs, and attitudes of these ECE teachers and if/how their perspectives influenced their educational career paths. Data were gathered through the triangulated use of participants' responses to a survey, face-to-face interviews, and a focus group. Findings demonstrate that the teachers understand that professional development, such as college coursework, can increase a person's knowledge on a given topic or field of study, but that they feel qualified to be a teacher for children birth to five even though 12 of the 15 teachers do not hold an AA/AAS or BA/BS degree in any area of study. Further, the teachers suggested that if they were to earn a degree it would most likely be in another field of study beside education. These responses provide another reason professional development efforts to encourage ECE teachers to seek degrees in the field of education may be failing. If ECE teachers wanted to invest time, energy and funds they would acquire a degree, which provided more financial reward and professional respect.
ContributorsOrtiz, Karen J. (Karen Jean) (Author) / Kelley, Michael F. (Thesis advisor) / Enz, Billie J. (Thesis advisor) / Romero, Mary (Committee member) / Fischman, Gustavo (Committee member) / Arizona State University (Publisher)
Created2011