2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)最新文献

Research Data Curation in Visualization : Position Paper 可视化中的研究数据管理:立场文件

2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2022-10-01 DOI: 10.1109/BELIV57783.2022.00011

Dimitar Garkov, C. Müller, Matthias Braun, D. Weiskopf, Falk Schreiber

引用次数: 0

How Personality and Visual Channels Affect Insight Generation 个性和视觉渠道如何影响洞察生成

2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2022-10-01 DOI: 10.1109/BELIV57783.2022.00010

Tomás Alves, Carlota Dias, D. Gonçalves, S. Gama, J. Henriques-Calado

引用次数: 0

Power Overwhelming: Quantifying the Energy Cost of Visualisation 压倒性的力量:量化可视化的能量成本

2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2022-10-01 DOI: 10.1109/BELIV57783.2022.00009

C. Müller, Moritz Heinemann, D. Weiskopf, T. Ertl

引用次数: 0

Preface to BELIV 2022 BELIV 2022前言

2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2022-10-01 DOI: 10.1109/beliv57783.2022.00016

引用次数: 0

Creative Visualisation Opportunities Workshops: A Case Study in Population Health 创意视觉化机会工作坊:人口健康个案研究

2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2022-10-01 DOI: 10.1109/BELIV57783.2022.00006

Mai Elshehaly, Kuldeep Sohal, T. Lawton, M. Bryant, M. Mon-Williams

{"title":"Creative Visualisation Opportunities Workshops: A Case Study in Population Health","authors":"Mai Elshehaly, Kuldeep Sohal, T. Lawton, M. Bryant, M. Mon-Williams","doi":"10.1109/BELIV57783.2022.00006","DOIUrl":"https://doi.org/10.1109/BELIV57783.2022.00006","url":null,"abstract":"Population Health Management (PHM) relies on the analysis of data from several sources to account for the complex interaction of factors that contribute to the health and well-being of a population, while considering biases and inequalities across sub-populations. Visualisation is emerging as an essential tool for insight generation from data shared and linked across services including healthcare, education, housing, policing, etc. However, visualisation design is challenged by poor data connectivity and quality, high dimensionality and complexity of real-world routinely collected data, in addition to the heterogeneity of users’ backgrounds and tasks. The Creative Visualisation Opportunities (CVO) framework provides a structured approach for working with diverse communities of visualisation stakeholders and defines a set of participatory activities for the effective elicitation of requirements and visualisation design alternatives. We conducted three workshops, applying variations of the CVO framework, with over one hundred participants from the PHM domain, including clinicians, researchers, government and private sector representatives, and local communities. In this paper, we present the results of preliminary analysis of these activities and report on the perceived impact of visualisation in this domain from a stakeholders’ perspective. We report real-world successes and limitations of applying the framework in different formats (through online and in-person workshops), and reflect on lessons learned for task analysis and visualisation design in the PHM domain.","PeriodicalId":299298,"journal":{"name":"2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128356109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

BELIV 2022 Program Committee BELIV 2022项目委员会

2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2022-10-01 DOI: 10.1109/beliv57783.2022.00017

引用次数: 0

How Do We Measure Trust in Visual Data Communication? 我们如何衡量视觉数据通信中的信任?

2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2022-09-28 DOI: 10.1109/BELIV57783.2022.00014

Hamza Elhamdadi, Aimen Gaba, Yea-Seul Kim, Cindy Xiong

{"title":"How Do We Measure Trust in Visual Data Communication?","authors":"Hamza Elhamdadi, Aimen Gaba, Yea-Seul Kim, Cindy Xiong","doi":"10.1109/BELIV57783.2022.00014","DOIUrl":"https://doi.org/10.1109/BELIV57783.2022.00014","url":null,"abstract":"Trust is fundamental to effective visual data communication between the visualization designer and the reader. Although personal experience and preference influence readers’ trust in visualizations, visualization designers can leverage design techniques to create visualizations that evoke a \"calibrated trust,\" at which readers arrive after critically evaluating the information presented. To systematically understand what drives readers to engage in \"calibrated trust,\" we must first equip ourselves with reliable and valid methods for measuring trust. Computer science and data visualization researchers have not yet reached a consensus on a trust definition or metric, which are essential to building a comprehensive trust model in human-data interaction. On the other hand, social scientists and behavioral economists have developed and perfected metrics that can measure generalized and interpersonal trust, which the visualization community can reference, modify, and adapt for our needs. In this paper, we gather existing methods for evaluating trust from other disciplines and discuss how we might use them to measure, define, and model trust in data visualization research. Specifically, we discuss quantitative surveys from social sciences, trust games from behavioral economics, measuring trust through measuring belief updating, and measuring trust through perceptual methods. We assess the potential issues with these methods and consider how we can systematically apply them to visualization research.","PeriodicalId":299298,"journal":{"name":"2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121634661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics: Position Paper 视觉文本分析评估与实验设计的跨学科视角:立场文件

2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2022-09-23 DOI: 10.1109/BELIV57783.2022.00008

K. Kucher, N. Sultanum, Angel Daza, Vasiliki Simaki, Maria Skeppstedt, Barbara Plank, Jean-Daniel Fekete, Narges Mahyar

{"title":"An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics: Position Paper","authors":"K. Kucher, N. Sultanum, Angel Daza, Vasiliki Simaki, Maria Skeppstedt, Barbara Plank, Jean-Daniel Fekete, Narges Mahyar","doi":"10.1109/BELIV57783.2022.00008","DOIUrl":"https://doi.org/10.1109/BELIV57783.2022.00008","url":null,"abstract":"Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of visual text analytics include concerns beyond computational or visual/interactive methods on their own. We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and \"big picture\" concerns) and provide suggestions for research opportunities from an interdisciplinary perspective.","PeriodicalId":299298,"journal":{"name":"2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127050873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Data-Centric Methodology and Task Typology for Time-Stamped Event Sequences 时间戳事件序列的数据中心方法和任务类型

2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2022-09-21 DOI: 10.1109/BELIV57783.2022.00012

Yasara Peiris, Clara-Maria Barth, Elaine M. Huang, J. Bernard

{"title":"A Data-Centric Methodology and Task Typology for Time-Stamped Event Sequences","authors":"Yasara Peiris, Clara-Maria Barth, Elaine M. Huang, J. Bernard","doi":"10.1109/BELIV57783.2022.00012","DOIUrl":"https://doi.org/10.1109/BELIV57783.2022.00012","url":null,"abstract":"Task abstractions and taxonomic structures for tasks are useful for designers of interactive data analysis approaches, serving as design targets and evaluation criteria alike. For individual data types, dataset-specific taxonomic structures capture unique data characteristics, while being generalizable across application domains. The creation of dataset-centric but domain-agnostic taxonomic structures is difficult, especially if best practices for a focused data type are still missing, observing experts is not feasible, and means for reflection and generalization are scarce. We discovered this need for methodological support when working with time-stamped event sequences, a datatype that has not yet been fully systematically studied in visualization research. To address this shortcoming, we present a methodology that enables researchers to abstract tasks and build dataset-centric taxonomic structures in five phases (data collection, coding, task categorization, task synthesis, and action-target-(criterion) crosscut). We validate the methodology by applying it to time-stamped event sequences and present a task typology that uses triples as a novel language of description for tasks: (1) action, (2) data target, and (3) data criterion. We further evaluate the descriptive power of the typology with a real-world case on cybersecurity.","PeriodicalId":299298,"journal":{"name":"2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121108699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Toward Inclusion and Accessibility in Visualization Research: Speculations on Challenges, Solution Strategies, and Calls for Action (Position Paper) 面向可视化研究的包容性和可及性:对挑战、解决方案策略和行动呼吁的推测(立场文件)

2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2022-09-12 DOI: 10.1109/BELIV57783.2022.00007

Katrin Angerbauer, M. Sedlmair

引用次数: 0