2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)最新文献

The Garden of Forking Paths in Visualization: A Design Space for Reliable Exploratory Visual Analytics : Position Paper 可视化中的分叉路径花园:可靠探索性视觉分析的设计空间:立场文件

2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2018-10-01 DOI: 10.1109/BELIV.2018.8634103

Xiaoying Pu, Matthew Kay

引用次数: 25

BELIV 2018 Preface

2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2018-10-01 DOI: 10.1109/beliv.2018.8634314

引用次数: 0

A Case for Cognitive Models in Visualization Research : Position paper 认知模型在视觉化研究中的案例:立场文件

2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2018-10-01 DOI: 10.1109/BELIV.2018.8634267

Lace M. K. Padilla

{"title":"A Case for Cognitive Models in Visualization Research : Position paper","authors":"Lace M. K. Padilla","doi":"10.1109/BELIV.2018.8634267","DOIUrl":"https://doi.org/10.1109/BELIV.2018.8634267","url":null,"abstract":"The visualization community has seen a rise in the adoption of user studies. Empirical user studies systematically test the assumptions that we make about how visualizations can help or hinder viewers’ performance of tasks. Although the increase in user studies is encouraging, it is vital that research on human reasoning with visualizations be grounded in an understanding of how the mind functions. Previously, there were no sufficient models that illustrate the process of decision-making with visualizations. However, Padilla et al. [41] recently proposed an integrative model for decision-making with visualizations, which expands on modern theories of visualization cognition and decision-making. In this paper, we provide insights into how cognitive models can accelerate innovation, improve validity, and facilitate replication efforts, which have yet to be thoroughly discussed in the visualization community. To do this, we offer a compact overview of the cognitive science of decision-making with visualizations for the visualization community, using the Padilla et al. [41] cognitive model as a guiding framework. By detailing examples of visualization research that illustrate each component of the model, this paper offers novel insights into how visualization researchers can utilize a cognitive framework to guide their user studies. We provide practical examples of each component of the model from empirical studies of visualizations, along with visualization implications of each cognitive process, which have not been directly addressed in prior work. Finally, this work offers a case study in utilizing an understanding of human cognition to generate a novel solution to a visualization reasoning bias in the context of hurricane forecast track visualizations.","PeriodicalId":269472,"journal":{"name":"2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123923014","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: 8

[Title page] (标题页)

2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2018-10-01 DOI: 10.1109/beliv.2018.8634286

引用次数: 0

From Taxonomy to Requirements: A Task Space Partitioning Approach 从分类法到需求:任务空间划分方法

2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2018-10-01 DOI: 10.1109/BELIV.2018.8634027

M. El-Shehaly, Natasha Alvarado, Lynn McVey, R. Randell, M. Mamas, R. Ruddle

{"title":"From Taxonomy to Requirements: A Task Space Partitioning Approach","authors":"M. El-Shehaly, Natasha Alvarado, Lynn McVey, R. Randell, M. Mamas, R. Ruddle","doi":"10.1109/BELIV.2018.8634027","DOIUrl":"https://doi.org/10.1109/BELIV.2018.8634027","url":null,"abstract":"We present a taxonomy-driven approach to requirements specification in a large-scale project setting, drawing on our work to develop visualization dashboards for improving the quality of healthcare. Our aim is to overcome some of the limitations of the qualitative methods that are typically used for requirements analysis. When applied alone, methods like interviews fall short in identifying the full set of functionalities that a visualization system should support. We present a five-stage pipeline to structure user task elicitation and analysis around well-established taxonomic dimensions, and make the following contributions: (i) criteria for selecting dimensions from the large body of task taxonomies in the literature,, (ii) use of three particular dimensions (granularity, type cardinality and target) to create materials for a requirements analysis workshop with domain experts, (iii) a method for characterizing the task space that was produced by the experts in the workshop, (iv) a decision tree that partitions that space and maps it to visualization design alternatives, and (v) validating our approach by testing the decision tree against new tasks that collected through interviews with further domain experts.","PeriodicalId":269472,"journal":{"name":"2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124973216","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: 3

How to Evaluate an Evaluation Study? Comparing and Contrasting Practices in Vis with Those of Other Disciplines : Position Paper 如何评估评估研究?Vis与其他学科实践的比较与对比:立场文件

2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2018-10-01 DOI: 10.1109/BELIV.2018.8634420

Anamaria Crisan, Madison Elliott

引用次数: 11

Towards Characterizing Domain Experts as a User Group 将领域专家定义为一个用户组

2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2018-10-01 DOI: 10.1109/BELIV.2018.8634026

Yuet Ling Wong, K. Madhavan, N. Elmqvist

{"title":"Towards Characterizing Domain Experts as a User Group","authors":"Yuet Ling Wong, K. Madhavan, N. Elmqvist","doi":"10.1109/BELIV.2018.8634026","DOIUrl":"https://doi.org/10.1109/BELIV.2018.8634026","url":null,"abstract":"Visualization is an inherently interdisciplinary research area: visualization researchers are always visualizing other people’s data. While recent trends in the field has turned toward a more inclusive audience, particularly within the topic of “visualization for the masses”, the traditional user group for our tools have been the domain expert: people who are experts in a specific professional domain where they want to apply visualization and analytics, but who often lack high literacy, training, and motivation in visualization and visual analytics. Such domain experts want to opportunistically reap the benefits of visualization, but have no patience for long training, poor interaction or visual design, or complex displays. While domain experts are a familiar user group, surprisingly little effort has been devoted towards characterizing them for both design and evaluation purposes. To help the visualization community better understand this specific group, in this paper, we describe the characteristics of domain experts, discuss existing examples designed for them, and propose possible guidelines to facilitate the design process. We believe this discussion will help visualization researchers better understand this group and uncover more research opportunities.","PeriodicalId":269472,"journal":{"name":"2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)","volume":"47 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113957802","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: 12

Heuristic Evaluation in Visualization: An Empirical Study : Position paper 可视化中的启发式评价:实证研究:立场文件

2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2018-10-01 DOI: 10.1109/BELIV.2018.8634108

B. Santos, S. Silva, Paulo Dias

引用次数: 8

Requirements for Reproducibility of Research in Situational and Spatio-Temporal Visualization : Position Paper 情境与时空可视化研究再现性的要求:立场文件

2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2018-10-01 DOI: 10.1109/BELIV.2018.8634150

André Calero Valdez, A. Schaar, J. Hildebrandt, M. Ziefle

引用次数: 2

Towards Designing Unbiased Replication Studies in Information Visualization 在信息可视化中设计无偏复制研究

2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV) Pub Date : 2018-10-01 DOI: 10.1109/BELIV.2018.8634261

P. Sukumar, Ronald A. Metoyer

{"title":"Towards Designing Unbiased Replication Studies in Information Visualization","authors":"P. Sukumar, Ronald A. Metoyer","doi":"10.1109/BELIV.2018.8634261","DOIUrl":"https://doi.org/10.1109/BELIV.2018.8634261","url":null,"abstract":"Experimenter bias and expectancy effects have been well studied in the social sciences and even in human-computer interaction. They refer to the nonideal study-design choices made by experimenters which can unfairly influence the outcomes of their studies. While these biases need to be considered when designing any empirical study, they can be particularly significant in the context of replication studies which can stray from the studies being replicated in only a few admissible ways. Although there are general guidelines for making valid, unbiased choices in each of the several steps in experimental design, making such choices when conducting replication studies has not been well explored.We reviewed 16 replication studies in information visualization published in four top venues between 2008 to present to characterize how the study designs of the replication studies differed from those of the studies they replicated. We present our characterization categories which include the prevalence of crowdsourcing, and the commonly-found replication types and study-design differences. We draw guidelines based on these categories towards helping researchers make meaningful and unbiased decisions when designing replication studies. Our paper presents the first steps in gaining a larger understanding of this topic and contributes to the ongoing efforts of encouraging researchers to conduct and publish more replication studies in information visualization.","PeriodicalId":269472,"journal":{"name":"2018 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127597579","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: 4