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interactive visual analytics for Sensemaking with Big Text

BIG DATA RESEARCH 2019年 16卷 49-58页

作者： Dowling, Michelle Wycoff, Nathan Mayer, Brian Wenskovitch, John Leman, Scotland House, Leanna Polys, Nicholas North, Chris Hauck, Peter

Analysts face many steep challenges when performing sensemaking tasks on collections of textual information larger than can be reasonably analyzed without computational assistance. To scale up such sensemaking tasks, new methods are needed to interactively integrate human cognitive sensemaking activity with machine learning. Towards that goal, we offer a human-in-the-loop computational model that mirrors the human sensemaking process, and consists of foraging and synthesis sub-processes. We model the synthesis loop as an interactive spatial projection and the foraging loop as an interactive relevance ranking combined with topic modeling. We combine these two components of the sensemaking process using semantic interaction such that the human's spatial synthesis actions are transformed into automated foraging and synthesis of new relevant information. Ultimately, the model's ability to forage as a result of the analyst's synthesis activities makes interacting with big text data easier and more efficient, thereby facilitating analysts' sensemaking ability. We discuss the interaction design and theory behind our interactive sensemaking model. The model is embodied in a novel visual analytics prototype called Cosmos in which analysts synthesize structure within the larger corpus by directly interacting with a reduced-dimensionality space to express relationships on a subset of data. We then demonstrate how Cosmos supports sensemaking tasks with a realistic scenario that investigates the affect of natural disasters in Adelaide, Australia in September 2016 using a database of over 30,000 news articles. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Text analytics Big data visualization interactive visual analytics Semantic interaction Topic modeling

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PHENOTREE: interactive visual analytics for Hierarchical Phenotyping From Large-Scale Electronic Health Records

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IEEE TRANSACTIONS ON MULTIMEDIA 2016年第11期18卷 2257-2270页

作者： Baytas, Inci M. Lin, Kaixiang Wang, Fei Jain, Anil K. Zhou, Jiayu Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA Cornell Univ Dept Healthcare Policy & Res Div Hlth Informat Ithaca NY 14850 USA

Electronic health records (EHRs) capture comprehensive patient information in digital form from a variety of sources. Increasing availability of EHRs has facilitated development of data and visual analytic tools for healthcare analytics, such as clinical decision support and patient care management systems. Many healthcare analytic tools are used to investigate fundamental problems, such as study of patient population, exploring complicated interactions among patients and their medical histories, and extracting structured phenotypes characterizing the patient population. In this paper, we propose PHENOTREE, a novel data-driven, hierarchical, and interactive phenotyping tool, that enables physicians and medical researchers to participate in the phenotyping process of large-scale EHR cohorts. The proposed visual analytic tool allows users to interactively explore EHR cohorts, and generate, interpret, evaluate, and refine phenotypes by building and navigating a phenotype hierarchy. Specifically, given a cohort or subcohort, PHENOTREE employs sparse principal component analysis (SPCA) to identify key clinical features that characterize the population. The clinical features provide a natural way to generate deeper phenotypes at finer granularities by expanding the phenotype hierarchy. To facilitate the intensive computation required for interactive analytics, we design an efficient SPCA solver based on a variance reduced stochastic gradient technique. The benefits of our method are demonstrated by analyzing two different EHR patient cohorts, a public and a private dataset containing EHRs of 101 767 and 223 076 patients, respectively. Our evaluations show that PHENOTREE can detect clinically meaningful hierarchical phenotypes.

关键词： driven phenotyping electronic health records (EHRs) hierarchical phenotyping interactive visual analytics sparse principal component analysis (SPCA)

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Understanding Failure Mode Effect Analysis Data Using interactive visual analytics

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2019年第6期39卷 17-26页

作者： Basole, Rahul C. Qamar, Ahsan Pal, Biswajyoti Corral, Michael Meinhart, Matthew Narechania, Arpit Georgia Inst Technol Coll Comp Atlanta GA 30332 USA Ford Motor Co Model Based Syst Engn Syst Engn Prod Dev Dearborn MI 48121 USA Georgia Inst Technol Sch Interact Comp Atlanta GA 30332 USA Ford Motor Co Res & Adv Engn Div Dearborn MI 48121 USA

Providing actionable insights through interactive visual analytics is essential to effective decision making. Yet, many complex systems engineering (SE) domains still lack such tools. Design reviews are often still based on static snapshots of data, without any dynamic interaction, data curation, and view creation capabilities to answer salient analysis questions. In this study, we report on a tool called DataHawk that helps answer common questions associated with one prominent SE context, namely failure mode and effect analysis (FMEA). The tool provides powerful exploration capabilities that enable system engineers, designers, and managers to probe FMEA data from multiple starting points, build questions dynamically, and find triangulated answers using multiple views rapidly. Field results are illustrated through a usage scenario from the automotive industry and show that the tool demonstrates the needed versatility, scalability, and effectiveness for real-world engineering data.

关键词： Data Analysis Data visualisation Decision Making Failure Analysis Software Engineering Systems Engineering System Engineers FMEA Data Real World Engineering Data interactive visual analytics Effective Decision Making Complex Systems Engineering Design Reviews Dynamic Interaction Data Curation Creation Capabilities Salient Analysis Questions Common Questions Prominent SE Context Failure Mode Effect Analysis Data Data Analysis visual analytics Complex Systems Vehicle Dynamics

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INPHOVIS:interactive visual analytics for smartphone-based digital phenotyping

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visual Informatics 2023年第2期7卷 13-29页

作者： Hamid Mansoor Walter Gerych Abdulaziz Alajaji Luke Buquicchio Kavin Chandrasekaran Emmanuel Agu Elke Rundensteiner Angela Incollingo Rodriguez Worcester Polytechnic Institute USA

Digital phenotyping is the characterization of human behavior patterns based on data from digital devices such as smartphones in order to gain insights into the users’state and especially to identify *** support supervised machine learning,digital phenotyping requires gathering data from study participants’smartphones as they live their ***,participants are then asked to provide ground truth labels about their health *** such complex data is challenging due to limited contextual information and imperfect health/wellness *** propose interactive PHOne-o-typing visualization(INPHOVIS),an interactive visual framework for exploratory analysis of smartphone health data to study *** visualization work has focused on mobile health data with clear semantics such as steps or heart rate data collected using dedicated health devices and wearables such as ***,unlike smartphones which are owned by over 85 percent of the US population,wearable devices are less prevalent thus reducing the number of people from whom such data can be *** contrast,the‘‘low-level"sensor data(e.g.,accelerometer or GPS data)supported by INPHOVIS can be easily collected using *** visualizations are designed to provide the essential contextualization of such data and thus help analysts discover complex relationships between observed sensor values and health-predictive *** guide the design of INPHOVIS,we performed a hierarchical task analysis of phone-o-typing requirements with health domain *** then designed and implemented multiple innovative visualizations integral to INPHOVIS including stacked bar charts to show diurnal behavioral patterns,calendar views to visualize day-level data along with bar charts,and correlation views to visualize important wellness predictive *** demonstrate the usefulness of INPHOVIS with walk-throughs of use *** also evaluated INPHOVIS with expert feedb

关键词： interactive visual analytics Smartphone-sensed data Digital phenotyping

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MyHealthAvatar : A Case Study of Web-based interactive visual analytics of Lifestyle Data 15

MyHealthAvatar : A Case Study of Web-based Interactive Visua...

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IEEE International Conference on Computer and Information

作者： Parvinzamir, Farzad Zhao, Youbing Deng, Zhikun Zhao, Xia Ersotelos, Nikolaos Dong, Feng Liu, Enjie Clapworthy, Gordon Univ Bedfordshire Dept Comp Sci & Technol Luton Beds England

ISBN: (纸本)9781509001545

MyHealthAvatar is a project designed to collect and track lifestyle and health data to promote citizen wellbeing. As a lifetime companion of citizens, the amount of data collected will be huge. It is almost impossible for citizen, patients and doctors to view, utilise and understand these data without proper visual presentation and user interaction. interactive visual analytics of lifestyle data is one of the key features of MyHealthAvatar. This paper presents the interactive visual analytics components in MyHealthAvatar to facilitate health and lifestyle data presentation and analysis, including 3d avatar, dashboard, diary, timeline, clock view and map. These components can be integrated to achieve flexible visual analysis of spatio-temporal lifestyle data.

关键词： MyHealthAvatar interactive visual analytics lifestyle data 3D avatar dashboard diary timeline ClockView map

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MyHealthAvatar : A Case Study of Web-based interactive visual analytics of Lifestyle Data

MyHealthAvatar : A Case Study of Web-based Interactive Visua...

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IEEE International Conference on Ubiquitous Computing and Communications

作者： Farzad Parvinzamir Youbing Zhao Zhikun Deng Xia Zhao Nikolaos Ersotelos Feng Dong Enjie Liu Gordon Clapworthy Department of Computer Science and Technology University of Bedfordshire Luton UK

ISBN: (纸本)9781509001552

关键词： MyHealthAvatar interactive visual analytics lifestyle data 3D avatar dashboard diary timeline ClockView map

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visualization Viewpoints Sampling for Scalable visual analytics

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2017年第1期37卷 100-108页

作者： Kwon, Bum Chul Verma, Janu Haas, Peter J. Demiralp, Cagatay IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA IBM Almaden Res Ctr San Jose CA USA

Sampling is becoming an essential tool for scalable interactive visual analysis. After outlining prior work by the database community on sampling for visualization of aggregation queries, this article considers how these results might be improved and extended to a broader setting. The goal is to better understand how users interact with sampling to enable wider adoption of sampling for scalable visual analytics.

关键词： Temperature Sensors Data visualization Uncertainty visual analytics visual Databases Sampling Computer Graphics visualization interactive visual analytics Scalable visualization Online Aggregation

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Many objective robust decision making for complex environmental systems undergoing change

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ENVIRONMENTAL MODELLING & SOFTWARE 2013年 42卷 55-71页

作者： Kasprzyk, Joseph R. Nataraj, Shanthi Reed, Patrick M. Lempert, Robert J. Penn State Univ Dept Civil & Environm Engn University Pk PA 16802 USA RAND Corp Santa Monica CA USA

This paper introduces many objective robust decision making (MORDM). MORDM combines concepts and methods from many objective evolutionary optimization and robust decision making (RDM), along with extensive use of interactive visual analytics, to facilitate the management of complex environmental systems. Many objective evolutionary search is used to generate alternatives for complex planning problems, enabling the discovery of the key tradeoffs among planning objectives. RDM then determines the robustness of planning alternatives to deeply uncertain future conditions and facilitates decision makers' selection of promising candidate solutions. MORDM tests each solution under the ensemble of future extreme states of the world (SOW). interactive visual analytics are used to explore whether solutions of interest are robust to a wide range of plausible future conditions (i.e., assessment of their Pareto satisficing behavior in alternative SOW). Scenario discovery methods that use statistical data mining algorithms are then used to identify what assumptions and system conditions strongly influence the cost-effectiveness, efficiency, and reliability of the robust alternatives. The framework is demonstrated using a case study that examines a single city's water supply in the Lower Rio Grande Valley (LRGV) in Texas, USA. Results suggest that including robustness as a decision criterion can dramatically change the formulation of complex environmental management problems as well as the negotiated selection of candidate alternatives to implement. MORDM also allows decision makers to characterize the most important vulnerabilities for their systems, which should be the focus of ex post monitoring and identification of triggers for adaptive management. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Multiobjective evolutionary algorithms Robust decision making interactive visual analytics Uncertainty analysis Water supply Environmental management

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Multi-aspect visual analytics on large-scale high-dimensional cyber security data

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INFORMATION visualIZATION 2015年第1期14卷 62-75页

作者： Chen, Victor Y. Razip, Ahmad M. Ko, Sungahn Qian, Cheryl Z. Ebert, David S. Purdue Univ Dept Comp Graph Technol W Lafayette IN 47907 USA Purdue Univ Dept Elect & Comp Engn W Lafayette IN 47907 USA Purdue Univ Dept Art & Design W Lafayette IN 47907 USA

In this article, we present a visual analytics system, SemanticPrism, which aims to analyze large-scale high-dimensional cyber security datasets containing logs of a million computers. SemanticPrism visualizes the data from three different perspectives: spatiotemporal distribution, overall temporal trends, and pixel-based IP (Internet Protocol) address blocks. With each perspective, we use semantic zooming to present more detailed information. The interlinked visualizations and multiple levels of detail allow us to detect unexpected changes taking place in different dimensions of the data and to identify potential anomalies in the network. After comparing our approach to other submissions, we outline potential paths for future improvement.

关键词： interactive visual analytics semantic zooming pixel oriented multivariate visualization geospatial analysis interaction design

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ARGUS: interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms

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visual Informatics 2021年第3期5卷 39-53页

作者： Hamid Mansoor Walter Gerych Abdulaziz Alajaji Luke Buquicchio Kavin Chandrasekaran Emmanuel Agu Elke Rundensteiner Worcester Polytechnic Institute USA

Human Bio-Behavioral Rhythms(HBRs)such as sleep-wake cycles(Circadian Rhythms),and the degree of regularity of sleep and physical activity have important health *** devices such as smartphones can sense HBRs by continuously analyzing data gathered passively by built-in sensors to discover important clues about the degree of regularity and disruptions in behavioral *** human behavior is complex and smartphone data is voluminous with many channels(sensor types),it can be challenging to make meaningful observations,detect unhealthy HBR deviations and most importantly pin-point the causes of *** work has largely utilized computational methods such as machine and deep learning approaches,which while accurate,are often not explainable and present few actionable insights on HBR patterns or *** assist analysts in the discovery and understanding of HBR patterns,disruptions and causes,we propose ARGUS,an interactive visual analytics *** a foundation of ARGUS,we design an intuitive Rhythm Deviation Score(RDS)that analyzes users’smartphone sensor data,extracts underlying twenty-four-hour rhythms and quantifies their degree of *** score is then visualized using a glyph that makes it easy to recognize disruptions in the regularity of *** also facilitates deeper HBR insights and understanding of causes by linking multiple visualization panes that are overlaid with objective sensor information such as geo-locations and phone state(screen locked,charging),and user-provided or smartphone-inferred ground truth *** array of visualization overlays in ARGUS enables analysts to gain a more comprehensive picture of HBRs,behavioral patterns and deviations from *** design of ARGUS was guided by a goal and task analysis study involving an expert versed in HBR and smartphone *** demonstrate its utility and generalizability,two different datasets were explored using ARGUS and our use ca

关键词： interactive visual analytics Circadian rhythms Smartphone-sensed data

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