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- All Subjects: Computer Science
- Creators: Maciejewski, Ross
Description
Education has been at the forefront of many issues in Arizona over the past several years with concerns over lack of funding sparking the Red for Ed movement. However, despite the push for educational change, there remain many barriers to education including a lack of visibility for how Arizona schools are performing at a legislative district level. While there are sources of information released at a school district level, many of these are limited and can become obscure to legislators when such school districts lie on the boundary between 2 different legislative districts. Moreover, much of this information is in the form of raw spreadsheets and is often fragmented between government websites and educational organizations. As such, a visualization dashboard that clearly identifies schools and their relative performance within each legislative district would be an extremely valuable tool to legislative bodies and the Arizona public. Although this dashboard and research are rough drafts of a larger concept, they would ideally increase transparency regarding public information about these districts and allow legislators to utilize the dashboard as a tool for greater understanding and more effective policymaking.
ContributorsColyar, Justin Dallas (Author) / Michael, Katina (Thesis director) / Maciejewski, Ross (Committee member) / Tate, Luke (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
In the last few years, there has been a tremendous increase in the use of big data. Most of this data is hard to understand because of its size and dimensions. The importance of this problem can be emphasized by the fact that Big Data Research and Development Initiative was announced by the United States administration in 2012 to address problems faced by the government. Various states and cities in the US gather spatial data about incidents like police calls for service.
When we query large amounts of data, it may lead to a lot of questions. For example, when we look at arithmetic relationships between queries in heterogeneous data, there are a lot of differences. How can we explain what factors account for these differences? If we define the observation as an arithmetic relationship between queries, this kind of problem can be solved by aggravation or intervention. Aggravation views the value of our observation for different set of tuples while intervention looks at the value of the observation after removing sets of tuples. We call the predicates which represent these tuples, explanations. Observations by themselves have limited importance. For example, if we observe a large number of taxi trips in a specific area, we might ask the question: Why are there so many trips here? Explanations attempt to answer these kinds of questions.
While aggravation and intervention are designed for non spatial data, we propose a new approach for explaining spatially heterogeneous data. Our approach expands on aggravation and intervention while using spatial partitioning/clustering to improve explanations for spatial data. Our proposed approach was evaluated against a real-world taxi dataset as well as a synthetic disease outbreak datasets. The approach was found to outperform aggravation in precision and recall while outperforming intervention in precision.
When we query large amounts of data, it may lead to a lot of questions. For example, when we look at arithmetic relationships between queries in heterogeneous data, there are a lot of differences. How can we explain what factors account for these differences? If we define the observation as an arithmetic relationship between queries, this kind of problem can be solved by aggravation or intervention. Aggravation views the value of our observation for different set of tuples while intervention looks at the value of the observation after removing sets of tuples. We call the predicates which represent these tuples, explanations. Observations by themselves have limited importance. For example, if we observe a large number of taxi trips in a specific area, we might ask the question: Why are there so many trips here? Explanations attempt to answer these kinds of questions.
While aggravation and intervention are designed for non spatial data, we propose a new approach for explaining spatially heterogeneous data. Our approach expands on aggravation and intervention while using spatial partitioning/clustering to improve explanations for spatial data. Our proposed approach was evaluated against a real-world taxi dataset as well as a synthetic disease outbreak datasets. The approach was found to outperform aggravation in precision and recall while outperforming intervention in precision.
ContributorsTahir, Anique (Author) / Elsayed, Mohamed (Thesis advisor) / Hsiao, Ihan (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2018
A Visual Analytics Process for Exploring Risk and Vulnerability in International Food Trade Networks
Description
The rise in globalization has led to regional climate events having an increased effect on global food security. These indirect first- and second-order effects are generally geographically disparate from the region experiencing the climate event. Without understanding the topology of the food trade network, international aid may be naively directed to the countries directly experiencing the climate event and not to countries that will face potential food insecurity due to that event. This thesis focuses on the development of a visual analytics system for exploring second-order effects of climate change under the lens of global trade. In order to visualize how climate change impacts the world trade network of agricultural goods I have developed an interactive data visualization platform for analysis of the interaction between climate events and the trade network. The proposed visual analytics system focuses on visualizing current trade dependencies at a more granular level than the currently available tools and to aid in the identification of future vulnerabilities. To demonstrate the applicability of the tool, two case studies are described. The first case study focuses on the Chinese drought of 2011 and its impact on the global trade network and food security. The second case study will model the potential impact of a climate event affecting production in the United States, a large supplier of corn, to demonstrate the potential consequence of cascading effects in the global trade network.
ContributorsSeville, Travis Allen (Author) / Maciejewski, Ross (Thesis advisor) / Hsiao, I-Han (Committee member) / Shutters, Shade (Committee member) / Arizona State University (Publisher)
Created2017
Description
Molecular Dynamics (MD) simulations are ubiquitous throughout the physical sci-ences; they are critical in understanding how particle structures evolve over time
given a particular energy function. A software package called ParSplice introduced a
new method to generate these simulations in parallel that has significantly inflated
their length. Typically, simulations are short discrete Markov chains, only captur-
ing a few microseconds of a particle’s behavior and containing tens of thousands of
transitions between states; in contrast, a typical ParSplice simulation can be as long
as a few milliseconds, containing tens of millions of transitions. Naturally, sifting
through data of this size is impossible by hand, and there are a number of visualiza-
tion systems that provide comprehensive and intuitive analyses of particle structures
throughout MD simulations. However, no visual analytics systems have been built
that can manage the simulations that ParSplice produces. To analyze these large
data-sets, I built a visual analytics system that provides multiple coordinated views
that simultaneously describe the data temporally, within its structural context, and
based on its properties. The system provides fluid and powerful user interactions
regardless of the size of the data, allowing the user to drill down into the data-set to
get detailed insights, as well as run and save various calculations, most notably the
Nudged Elastic Band method. The system also allows the comparison of multiple
trajectories, revealing more information about the general behavior of particles at different temperatures, energy states etc.
ContributorsHnatyshyn, Rostyslav (Author) / Maciejewski, Ross (Thesis advisor) / Bryan, Chris (Committee member) / Ahrens, James (Committee member) / Arizona State University (Publisher)
Created2022
Description
Coastal areas are susceptible to man-made disasters, such as oil spills, which not
only have a dreadful impact on the lives of coastal communities and businesses but also
have lasting and hazardous consequences. The United States coastal areas, especially
the Gulf of Mexico, have witnessed devastating oil spills of varied sizes and durations
that resulted in major economic and ecological losses. These disasters affected the oil,
housing, forestry, tourism, and fishing industries with overall costs exceeding billions
of dollars (Baade et al. (2007); Smith et al. (2011)). Extensive research has been
done with respect to oil spill simulation techniques, spatial optimization models, and
innovative strategies to deal with spill response and planning efforts. However, most
of the research done in those areas is done independently of each other, leaving a
conceptual void between them.
In the following work, this thesis presents a Spatial Decision Support System
(SDSS), which efficiently integrates the independent facets of spill modeling techniques
and spatial optimization to enable officials to investigate and explore the various
options to clean up an offshore oil spill to make a more informed decision. This
thesis utilizes Blowout and Spill Occurrence Model (BLOSOM) developed by Sim
et al. (2015) to simulate hypothetical oil spill scenarios, followed by the Oil Spill
Cleanup and Operational Model (OSCOM) developed by Grubesic et al. (2017) to
spatially optimize the response efforts. The results of this combination are visualized
in the SDSS, featuring geographical maps, so the boat ramps from which the response
should be launched can be easily identified along with the amount of oil that hits the
shore thereby visualizing the intensity of the impact of the spill in the coastal areas
for various cleanup targets.
only have a dreadful impact on the lives of coastal communities and businesses but also
have lasting and hazardous consequences. The United States coastal areas, especially
the Gulf of Mexico, have witnessed devastating oil spills of varied sizes and durations
that resulted in major economic and ecological losses. These disasters affected the oil,
housing, forestry, tourism, and fishing industries with overall costs exceeding billions
of dollars (Baade et al. (2007); Smith et al. (2011)). Extensive research has been
done with respect to oil spill simulation techniques, spatial optimization models, and
innovative strategies to deal with spill response and planning efforts. However, most
of the research done in those areas is done independently of each other, leaving a
conceptual void between them.
In the following work, this thesis presents a Spatial Decision Support System
(SDSS), which efficiently integrates the independent facets of spill modeling techniques
and spatial optimization to enable officials to investigate and explore the various
options to clean up an offshore oil spill to make a more informed decision. This
thesis utilizes Blowout and Spill Occurrence Model (BLOSOM) developed by Sim
et al. (2015) to simulate hypothetical oil spill scenarios, followed by the Oil Spill
Cleanup and Operational Model (OSCOM) developed by Grubesic et al. (2017) to
spatially optimize the response efforts. The results of this combination are visualized
in the SDSS, featuring geographical maps, so the boat ramps from which the response
should be launched can be easily identified along with the amount of oil that hits the
shore thereby visualizing the intensity of the impact of the spill in the coastal areas
for various cleanup targets.
ContributorsPydi Medini, Prannoy Chandra (Author) / Maciejewski, Ross (Thesis advisor) / Grubesic, Anthony (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2018
Description
Machine learning models are increasingly being deployed in real-world applications where their predictions are used to make critical decisions in a variety of domains. The proliferation of such models has led to a burgeoning need to ensure the reliability and safety of these models, given the potential negative consequences of model vulnerabilities. The complexity of machine learning models, along with the extensive data sets they analyze, can result in unpredictable and unintended outcomes. Model vulnerabilities may manifest due to errors in data input, algorithm design, or model deployment, which can have significant implications for both individuals and society. To prevent such negative outcomes, it is imperative to identify model vulnerabilities at an early stage in the development process. This will aid in guaranteeing the integrity, dependability, and safety of the models, thus mitigating potential risks and enabling the full potential of these technologies to be realized. However, enumerating vulnerabilities can be challenging due to the complexity of the real-world environment. Visual analytics, situated at the intersection of human-computer interaction, computer graphics, and artificial intelligence, offers a promising approach for achieving high interpretability of complex black-box models, thus reducing the cost of obtaining insights into potential vulnerabilities of models. This research is devoted to designing novel visual analytics methods to support the identification and analysis of model vulnerabilities. Specifically, generalizable visual analytics frameworks are instantiated to explore vulnerabilities in machine learning models concerning security (adversarial attacks and data perturbation) and fairness (algorithmic bias). In the end, a visual analytics approach is proposed to enable domain experts to explain and diagnose the model improvement of addressing identified vulnerabilities of machine learning models in a human-in-the-loop fashion. The proposed methods hold the potential to enhance the security and fairness of machine learning models deployed in critical real-world applications.
ContributorsXie, Tiankai (Author) / Maciejewski, Ross (Thesis advisor) / Liu, Huan (Committee member) / Bryan, Chris (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2023
Description
Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models. This thesis explores using concepts from computational conformal geometry to create a custom software framework for examining and generating quantitative mathematical models for characterizing the geometry of early visual areas in the human brain. The software framework includes a graphical user interface built on top of a selected core conformal flattening algorithm and various software tools compiled specifically for processing and examining retinotopic data. Three conformal flattening algorithms were implemented and evaluated for speed and how well they preserve the conformal metric. All three algorithms performed well in preserving the conformal metric but the speed and stability of the algorithms varied. The software framework performed correctly on actual retinotopic data collected using the standard travelling-wave experiment. Preliminary analysis of the Beltrami coefficient for the early data set shows that selected regions of V1 that contain reasonably smooth eccentricity and polar angle gradients do show significant local conformality, warranting further investigation of this approach for analysis of early and higher visual cortex.
ContributorsTa, Duyan (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Wonka, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
Vectorization is an important process in the fields of graphics and image processing. In computer-aided design (CAD), drawings are scanned, vectorized and written as CAD files in a process called paper-to-CAD conversion or drawing conversion. In geographic information systems (GIS), satellite or aerial images are vectorized to create maps. In graphic design and photography, raster graphics can be vectorized for easier usage and resizing. Vector arts are popular as online contents. Vectorization takes raster images, point clouds, or a series of scattered data samples in space, outputs graphic elements of various types including points, lines, curves, polygons, parametric curves and surface patches. The vectorized representations consist of a different set of components and elements from that of the inputs. The change of representation is the key difference between vectorization and practices such as smoothing and filtering. Compared to the inputs, the vector outputs provide higher order of control and attributes such as smoothness. Their curvatures or gradients at the points are scale invariant and they are more robust data sources for downstream applications and analysis. This dissertation explores and broadens the scope of vectorization in various contexts. I propose a novel vectorization algorithm on raster images along with several new applications for vectorization mechanism in processing and analysing both 2D and 3D data sets. The main components of the research are: using vectorization in generating 3D models from 2D floor plans; a novel raster image vectorization methods and its applications in computer vision, image processing, and animation; and vectorization in visualizing and information extraction in 3D laser scan data. I also apply vectorization analysis towards human body scans and rock surface scans to show insights otherwise difficult to obtain.
ContributorsYin, Xuetao (Author) / Razdan, Anshuman (Thesis advisor) / Wonka, Peter (Committee member) / Femiani, John (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2016
Description
Causality analysis is the process of identifying cause-effect relationships among variables. This process is challenging because causal relationships cannot be tested solely based on statistical indicators as additional information is always needed to reduce the ambiguity caused by factors beyond those covered by the statistical test. Traditionally, controlled experiments are carried out to identify causal relationships, but recently there is a growing interest in causality analysis with observational data due to the increasing availability of data and tools. This type of analysis will often involve automatic algorithms that extract causal relations from large amounts of data and rely on expert judgment to scrutinize and verify the relations. Over-reliance on these automatic algorithms is dangerous because models trained on observational data are susceptible to bias that can be difficult to spot even with expert oversight. Visualization has proven to be effective at bridging the gap between human experts and statistical models by enabling an interactive exploration and manipulation of the data and models. This thesis develops a visual analytics framework to support the interaction between human experts and automatic models in causality analysis. Three case studies were conducted to demonstrate the application of the visual analytics framework in which feature engineering, insight generation, correlation analysis, and causality inspections were showcased.
ContributorsWang, Hong, Ph.D (Author) / Maciejewski, Ross (Thesis advisor) / He, Jingrui (Committee member) / Davulcu, Hasan (Committee member) / Thies, Cameron (Committee member) / Arizona State University (Publisher)
Created2019
Description
In recent years, the rise in social media usage both vertically in terms of the number of users by platform and horizontally in terms of the number of platforms per user has led to data explosion.
User-generated social media content provides an excellent opportunity to mine data of interest and to build resourceful applications. The rise in the number of healthcare-related social media platforms and the volume of healthcare knowledge available online in the last decade has resulted in increased social media usage for personal healthcare. In the United States, nearly ninety percent of adults, in the age group 50-75, have used social media to seek and share health information. Motivated by the growth of social media usage, this thesis focuses on healthcare-related applications, study various challenges posed by social media data, and address them through novel and effective machine learning algorithms.
The major challenges for effectively and efficiently mining social media data to build functional applications include: (1) Data reliability and acceptance: most social media data (especially in the context of healthcare-related social media) is not regulated and little has been studied on the benefits of healthcare-specific social media; (2) Data heterogeneity: social media data is generated by users with both demographic and geographic diversity; (3) Model transparency and trustworthiness: most existing machine learning models for addressing heterogeneity are considered as black box models, not many providing explanations for why they do what they do to trust them.
In response to these challenges, three main research directions have been investigated in this thesis: (1) Analyzing social media influence on healthcare: to study the real world impact of social media as a source to offer or seek support for patients with chronic health conditions; (2) Learning from task heterogeneity: to propose various models and algorithms that are adaptable to new social media platforms and robust to dynamic social media data, specifically on modeling user behaviors, identifying similar actors across platforms, and adapting black box models to a specific learning scenario; (3) Explaining heterogeneous models: to interpret predictive models in the presence of task heterogeneity. In this thesis, novel algorithms with theoretical analysis from various aspects (e.g., time complexity, convergence properties) have been proposed. The effectiveness and efficiency of the proposed algorithms is demonstrated by comparison with state-of-the-art methods and relevant case studies.
User-generated social media content provides an excellent opportunity to mine data of interest and to build resourceful applications. The rise in the number of healthcare-related social media platforms and the volume of healthcare knowledge available online in the last decade has resulted in increased social media usage for personal healthcare. In the United States, nearly ninety percent of adults, in the age group 50-75, have used social media to seek and share health information. Motivated by the growth of social media usage, this thesis focuses on healthcare-related applications, study various challenges posed by social media data, and address them through novel and effective machine learning algorithms.
The major challenges for effectively and efficiently mining social media data to build functional applications include: (1) Data reliability and acceptance: most social media data (especially in the context of healthcare-related social media) is not regulated and little has been studied on the benefits of healthcare-specific social media; (2) Data heterogeneity: social media data is generated by users with both demographic and geographic diversity; (3) Model transparency and trustworthiness: most existing machine learning models for addressing heterogeneity are considered as black box models, not many providing explanations for why they do what they do to trust them.
In response to these challenges, three main research directions have been investigated in this thesis: (1) Analyzing social media influence on healthcare: to study the real world impact of social media as a source to offer or seek support for patients with chronic health conditions; (2) Learning from task heterogeneity: to propose various models and algorithms that are adaptable to new social media platforms and robust to dynamic social media data, specifically on modeling user behaviors, identifying similar actors across platforms, and adapting black box models to a specific learning scenario; (3) Explaining heterogeneous models: to interpret predictive models in the presence of task heterogeneity. In this thesis, novel algorithms with theoretical analysis from various aspects (e.g., time complexity, convergence properties) have been proposed. The effectiveness and efficiency of the proposed algorithms is demonstrated by comparison with state-of-the-art methods and relevant case studies.
ContributorsNelakurthi, Arun Reddy (Author) / He, Jingrui (Thesis advisor) / Cook, Curtiss B (Committee member) / Maciejewski, Ross (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2019