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Efficient Java native interface for android based mobile devices

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…

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

Enhancing the usability of complex structured data by supporting keyword searches

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…

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

Association based prioritization of genes

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…

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

Economic Analysis of Expenditure for Covid-19

Description

This thesis was conducted to study and analyze the fund allocation process adopted by different states in the United States to reduce the impact of the Covid-19 virus. Seven different states and their funding methodologies were compared against the case count within the state. The study also focused on development…

This thesis was conducted to study and analyze the fund allocation process adopted by different states in the United States to reduce the impact of the Covid-19 virus. Seven different states and their funding methodologies were compared against the case count within the state. The study also focused on development of a physical distancing index based on three significant attributes. This index was then compared to the expenditure and case counts to support decision making.
A regression model was developed to analyze and compare how different states case counts played out against the regression model and the risk index.

ContributorsJaisinghani, Shaurya (Author) / Mirchandani, Pitu (Thesis director) / Clough, Michael (Committee member) / McCarville, Daniel R. (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Department of Information Systems (Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Privacy Valuation Experiment

Description

This survey takes information on a participant’s beliefs on privacy security, the general digital knowledge, demographics, and willingness-to-pay points on if they would delete information on their social media, to see how an information treatment affects those payment points. This information treatment is meant to make half of the participants…

This survey takes information on a participant’s beliefs on privacy security, the general digital knowledge, demographics, and willingness-to-pay points on if they would delete information on their social media, to see how an information treatment affects those payment points. This information treatment is meant to make half of the participants think about the deeper ramifications of the information they reveal. The initial hypothesis is that this information will make people want to pay more to remove their information from the web, but the results find a surprising negative correlation with the treatment.

ContributorsDeitrick, Noah Sumner (Author) / Silverman, Daniel (Thesis director) / Kuminoff, Nicolai (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

The Art of the Novella

Description

For this Creative Project, I decided to explore the elements that set novellas apart from other genres and then experiment writing in the form. In doing so, I took into account three main categories: Plot Structure, Character Development, Style/Format, and then used my findings to write 45 pages of a…

For this Creative Project, I decided to explore the elements that set novellas apart from other genres and then experiment writing in the form. In doing so, I took into account three main categories: Plot Structure, Character Development, Style/Format, and then used my findings to write 45 pages of a novella titled Emmy and Me.

ContributorsBingham, Roxanne Marie (Author) / Irish, Jennifer (Thesis director) / Danielson, Jonathan (Committee member) / Department of English (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Art Education For Incarcerated Youth

Description

My project is designed to provide art education to incarcerated youth in Arizona. This project will address two current issues in Arizona; the underfunding of art programs and high rates of incarceration. As of 2021, there are no state-funded art programs in Arizona. Arizona is tied with Texas for the…

My project is designed to provide art education to incarcerated youth in Arizona. This project will address two current issues in Arizona; the underfunding of art programs and high rates of incarceration. As of 2021, there are no state-funded art programs in Arizona. Arizona is tied with Texas for the eighth highest rate of incarceration in the country. In Arizona, 750 out of every 100,000 people are incarcerated. This project is an art course for incarcerated youth. The project includes a packet detailing the course content and assignment details, a class syllabus, a course flyer, and a certificate of completion. The course is intended to be taught at the Adobe Mountain School facility. The course is designed so that it can be implemented in other facilities in the future. The class will be taught by volunteers with a background in studio art, design, or art education. Each student will receive a course packet that they can use to keep track of information and assignments. Instructors will use the course packet to teach the class. The course focuses on drawing with charcoal and oil pastel, which will build a foundation in drawing skills. The course covers a twelve-week semester. The course content packet includes a week-by-week breakdown of the teaching material and project descriptions. The course consists of two main projects and preparatory work. The preparatory work includes vocabulary terms, art concepts, drawing guides, brainstorming activities, and drawing activities. The two main prompts are designed for students to explore the materials and to encourage self-reflection. The class is curated so that students can create art in a low-risk, non-judgemental environment. The course will also focus on establishing problem-solving and critical thinking skills through engaging activities.

ContributorsSheppard, Eve (Author) / Cornelia, Wells (Thesis director) / Jennifer, Nelson (Committee member) / School of Art (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

ACC/CSE Thesis Project

Description

Developed a business product with a team of CS students.

ContributorsPerri, Cole Thomas (Co-author) / Hernandez, Maximilliano (Co-author) / Schneider, Kaitlin (Co-author) / Call, Andy (Thesis director) / Hunt, Neil (Committee member) / School of Accountancy (Contributor) / Watts College of Public Service & Community Solut (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

The Verde River Watershed: Using Geospatial Web Applications for a Virtual Learning Experience

Description

The COVID-19 Pandemic has provided a challenge for educators to create virtual learning materials that are engaging and impactful during times of high stress and isolation. In this creative project, I explore the variety of virtual tools and web applications from Esri by creating a Story Map on the Verde…

The COVID-19 Pandemic has provided a challenge for educators to create virtual learning materials that are engaging and impactful during times of high stress and isolation. In this creative project, I explore the variety of virtual tools and web applications from Esri by creating a Story Map on the Verde River Watershed. This Story Map is intended for an audience of students in late middle school and early high school but can be a resource to teachers for a wider age range. The integration of interactive technology and virtual tools in educational practices is likely to continue past the immediate circumstances of the COVID-19 pandemic. The purpose of this Story Map is to showcase one of the many uses for geospatial web applications beyond the immediate realm of GIS.

ContributorsTueller, Margaret (Author) / Frazier, Amy (Thesis director) / Dorn, Ron (Committee member) / School of Geographical Sciences and Urban Planning (Contributor, Contributor, Contributor) / Division of Teacher Preparation (Contributor) / The Design School (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

CPR complex pattern ranking for evaluating top-k pattern queries over event streams

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…

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

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