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Browsing by Subject "Visual analytics"
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Item Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations(Association for Computer Machinery, 2019) Choi, In Kwon; Childers, Taylor; Raveendranath, Nirmal Kumar; Mishra, Swati; Harris, Kyle; Reda, KhairiVisualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.Item DAG-based Task Orchestration for Edge Computing(IEEE, 2022) Li, Xiang; Abdallah, Mustafa; Suryavansh, Shikhar; Chiang, Mung; Bagchi, Saurabh; Engineering Technology, Purdue School of Engineering and TechnologyEdge computing promises to exploit underlying computation resources closer to users to help run latency-sensitive applications such as augmented reality and video analytics. However, one key missing piece has been how to incorporate personally owned, unmanaged devices into a usable edge computing system. The primary challenges arise due to the heterogeneity, lack of interference management, and unpredictable availability of such devices. In this paper we propose an orchestration framework IBDASH, which orchestrates application tasks on an edge system that comprises a mix of commercial and personal edge devices. IBDASH targets reducing both end-to-end latency of execution and probability of failure for applications that have dependency among tasks, captured by directed acyclic graphs (DAGs). IBDASH takes memory constraints of each edge device and network bandwidth into consideration. To assess the effectiveness of IBDASH, we run real application tasks on real edge devices with widely varying capabilities. We feed these measurements into a simulator that runs IBDASH at scale. Compared to three state-of-the-art edge orchestration schemes and two intuitive baselines, IBDASH reduces the end-to-end latency and probability of failure, by 14% and 41% on average respectively. The main takeaway from our work is that it is feasible to combine personal and commercial devices into a usable edge computing platform, one that delivers low and predictable latency and high availability.Item Iterative Visual Analytics and its Applications in Bioinformatics(2012-03-20) You, Qian; Fang, Shiaofen; Si, Luo; Tuceryan, Mihran; Sacks, ElishaYou, Qian. Ph.D., Purdue University, December, 2010. Iterative Visual Analytics and its Applications in Bioinformatics. Major Professors: Shiaofen Fang and Luo Si. Visual Analytics is a new and developing field that addresses the challenges of knowledge discoveries from the massive amount of available data. It facilitates humans‘ reasoning capabilities with interactive visual interfaces for exploratory data analysis tasks, where automatic data mining methods fall short due to the lack of the pre-defined objective functions. Analyzing the large volume of data sets for biological discoveries raises similar challenges. The domain knowledge of biologists and bioinformaticians is critical in the hypothesis-driven discovery tasks. Yet developing visual analytics frameworks for bioinformatic applications is still in its infancy. In this dissertation, we propose a general visual analytics framework – Iterative Visual Analytics (IVA) – to address some of the challenges in the current research. The framework consists of three progressive steps to explore data sets with the increased complexity: Terrain Surface Multi-dimensional Data Visualization, a new multi-dimensional technique that highlights the global patterns from the profile of a large scale network. It can lead users‘ attention to characteristic regions for discovering otherwise hidden knowledge; Correlative Multi-level Terrain Surface Visualization, a new visual platform that provides the overview and boosts the major signals of the numeric correlations among nodes in interconnected networks of different contexts. It enables users to gain critical insights and perform data analytical tasks in the context of multiple correlated networks; and the Iterative Visual Refinement Model, an innovative process that treats users‘ perceptions as the objective functions, and guides the users to form the optimal hypothesis by improving the desired visual patterns. It is a formalized model for interactive explorations to converge to optimal solutions. We also showcase our approach with bio-molecular data sets and demonstrate its effectiveness in several biomarker discovery applications.Item Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers(Springer, 2018-09-04) Kunjan, Kislaya; Wu, Huanmei; Toscos, Tammy R.; Doebbeling, Bradley N.; BioHealth Informatics, School of Informatics and ComputingPatient-centered appointment access is of critical importance at community health centers (CHCs) and its optimal implementation entails the use of advanced data analytics. This study seeks to optimize patient-centered appointment scheduling through data mining of Electronic Health Record/Practice Management (EHR/PM) systems. Data was collected from different EHR/PM systems in use at three CHCs across the state of Indiana and integrated into a multidimensional data warehouse. Data mining was performed using decision tree modeling, logistic regression, and visual analytics combined with n-gram modeling to derive critical influential factors that guide implementation of patient-centered open-access scheduling. The analysis showed that appointment adherence was significantly correlated with the time dimension of scheduling, with lead time for an appointment being the most significant predictor. Other variables in the time dimension such as time of the day and season were important predictors as were variables tied to patient demographic and clinical characteristics. Operationalizing the findings for selection of open-access hours led to a 16% drop in missed appointment rates at the interventional health center. The study uncovered the variability in factors affecting patient appointment adherence and associated open-access interventions in different health care settings. It also shed light on the reasons for same-day appointment through n-gram-based text mining. Optimizing open-access scheduling methods require ongoing monitoring and mining of large-scale appointment data to uncover significant appointment variables that impact schedule utilization. The study also highlights the need for greater "in-CHC" data analytic capabilities to re-design care delivery processes for improving access and efficiency.Item Towards Concept-Driven Visual Analytics(IEEE, 2018) Choi, In Kwon; Mishra, Swati; Harris, Kyle; Raveendranath, Nirmal Kumar; Childers, Taylor; Reda, KhairiVisualizations of data provide a proven method for analysts to explore and make data-driven discoveries. However, current visualization tools provide only limited support for hypothesis-driven analyses, and often lack capabilities that would allow users to visually test the fit of their conceptual models against the data. This imbalance could bias users to overly rely on exploratory visual analysis as the principal mode of inquiry, which can be detrimental to discovery. To address this gap, we propose a new paradigm for 'concept-driven' visual analysis. In this style of analysis, analysts share their conceptual models and hypotheses with the system. The system then uses those inputs to drive the generation of visualizations, while providing plots and interactions to explore places where models and data disagree. We discuss key characteristics and design considerations for concept-driven visualizations, and report preliminary findings from a formative study.Item TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease(Oxford University Press, 2024) Li, Zuotian; Liu, Xiang; Tang, Ziyang; Jin, Nanxin; Zhang, Pengyue; Eadon, Michael T.; Song, Qianqian; Chen, Yingjie V.; Su, Jing; Biostatistics and Health Data Science, School of MedicineObjective: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression. Materials and methods: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. Results: The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. Discussion: The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations. Conclusion: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.Item Visual Analytics and Interactive Machine Learning for Human Brain Data(2019-08) Li, Huang; Fang, Shiaofen; Shen, Li; Mukhopadhyay, SnehasisThis study mainly focuses on applying visualization techniques on human brain data for data exploration, quality control, and hypothesis discovery. It mainly consists of two parts: multi-modal data visualization and interactive machine learning. For multi-modal data visualization, a major challenge is how to integrate structural, functional and connectivity data to form a comprehensive visual context. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. For interactive machine learning, we propose a new visual analytics approach to interactive machine learning. In this approach, multi-dimensional data visualization techniques are employed to facilitate user interactions with the machine learning process. This allows dynamic user feedback in different forms, such as data selection, data labeling, and data correction, to enhance the efficiency of model building.Item Visual Analytics for Data-Driven Understanding of the Substance Use Disorder Epidemic(Sage, 2024) Qaurooni, Danial; Herr, Bruce W., II; Zappone, Sarah Renee; Wojciechowska, Klaudia; Börner, Katy; Schleyer, Titus; Medicine, School of MedicineThe substance use disorder epidemic has emerged as a serious public health crisis, presenting complex challenges. Visual analytics offers a unique approach to address this complexity and facilitate effective interventions. This paper details the development of an innovative visual analytics dashboard, aimed at enhancing our understanding of the substance use disorder epidemic. By employing record linkage techniques, we integrate diverse data sources to provide a comprehensive view of the epidemic. Adherence to responsive, open, and user-centered design principles ensures the dashboard's usefulness and usability. Our approach to data and design encourages collaboration among various stakeholders, including researchers, politicians, and healthcare practitioners. Through illustrative outputs, we demonstrate how the dashboard can deepen our understanding of the epidemic, support intervention strategies, and evaluate the effectiveness of implemented measures. The paper concludes with a discussion of the dashboard's use cases and limitations.Item A visual Analytics System for Optimizing Communications in Massively Parallel Applications(IEEE, 2017) Fujiwara, Takanori; Malakar, Preeti; Reda, Khairi; Vishwanath, Venkatram; Papka, Michael E.; Ma, Kwan-LiuCurrent and future supercomputers have tens of thousands of compute nodes interconnected with high-dimensional networks and complex network topologies for improved performance. Application developers are required to write scalable parallel programs in order to achieve high throughput on these machines. Application performance is largely determined by efficient inter-process communication. A common way to analyze and optimize performance is through profiling parallel codes to identify communication bottlenecks. However, understanding gigabytes of profile data is not a trivial task. In this paper, we present a visual analytics system for identifying the scalability bottlenecks and improving the communication efficiency of massively parallel applications. Visualization methods used in this system are designed to comprehend large-scale and varied communication patterns on thousands of nodes in complex networks such as the 5D torus and the dragonfly. We also present efficient rerouting and remapping algorithms that can be coupled with our interactive visual analytics design for performance optimization. We demonstrate the utility of our system with several case studies using three benchmark applications on two leading supercomputers. The mapping suggestion from our system led to 38% improvement in hop-bytes for MiniAMR application on 4,096 MPI processes.