Information Visualization最新文献

{"title":"Stable visualization of connected components in dynamic graphs","authors":"E. D. Giacomo, W. Didimo, M. Kaufmann, G. Liotta","doi":"10.1177/1473871620972339","DOIUrl":"https://doi.org/10.1177/1473871620972339","url":null,"abstract":"One of the primary goals of many systems for the visual analysis of dynamically changing networks is to maintain the stability of the drawing throughout the sequence of graph changes. We investigate the scenario where the changes are determined by a stream of events, each being either an edge addition or an edge removal. The visualization must be updated immediately after each new event is received. Our main goal is to provide the user with an intuitive visualization that highlights the different connected components of the graph while preserving the user’s mental map after each event. The drawing stability is measured in terms of changes in the orthogonal relationships between vertices of two consecutive drawings. We describe two different visualization models, one for the 1-dimensional space and the other for the 2-dimensional space. In both models the connected components are drawn inside rectangular regions. To validate our approach, we report the results of an experimental analysis that compares the drawing stability of the online algorithm with that of an offline algorithm that knows in advance the whole sequence of events. We also present a case study of our online algorithm on a collaboration network.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871620972339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41961465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of tag clouds as tools for information retrieval and content representation","authors":"Úrsula Torres-Parejo, Jesús R. Campaña, M. Vila, M. Delgado","doi":"10.1177/1473871620966638","DOIUrl":"https://doi.org/10.1177/1473871620966638","url":null,"abstract":"Tag clouds are tools that have been widely used on the Internet since their conception. The main applications of these textual visualizations are information retrieval, content representation and browsing of the original text from which the tags are generated. Despite the extensive use of tag clouds, their enormous popularity and the amount of research related to different aspects of them, few studies have summarized their most important features when they work as tools for information retrieval and content representation. In this paper we present a summary of the main characteristics of tag clouds found in the literature, such as their different functions, designs and negative aspects. We also present a summary of the most popular metrics used to capture the structural properties of a tag cloud generated from the query results, as well as other measures for evaluating the goodness of the tag cloud when it works as a tool for content representation. The different methods for tagging and the semantic association processes in tag clouds are also considered. Finally we give a list of alternative for visual interfaces, which makes this study a useful first help for researchers who want to study the content representation and information retrieval interfaces in greater depth.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871620966638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45095599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sanguine: Visual analysis for patient blood management","authors":"Haihan Lin, R. Metcalf, Jack T. Wilburn, A. Lex","doi":"10.1177/14738716211028565","DOIUrl":"https://doi.org/10.1177/14738716211028565","url":null,"abstract":"Blood transfusion is a frequently performed medical procedure in surgical and nonsurgical contexts. Although it is often necessary or even life-saving, it has been identified as one of the most overused procedures in hospitals. Unnecessary transfusions not only waste resources but can also be detrimental to patient outcomes. Patient blood management (PBM) is the clinical practice of optimizing transfusions and associated outcomes. In this paper, we introduce Sanguine, a visual analysis tool for transfusion data and related patient medical records. Sanguine was designed with two user groups in mind: PBM experts who oversee blood management practices across an institution and clinicians performing transfusions. PBM experts use Sanguine to explore and analyze transfusion practices and their associated medical outcomes. They can compare individual surgeons, or compare outcomes or time periods, such as before and after an intervention regarding transfusion practices. PBM experts then curate and annotate views for communication with clinicians, with the goal of improving their transfusion practices. We validate the utility and effectiveness of Sanguine through case studies.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/14738716211028565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45924467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualisation of law and legal Process: An opportunity missed","authors":"S. McLachlan, L. Webley","doi":"10.1177/14738716211012608","DOIUrl":"https://doi.org/10.1177/14738716211012608","url":null,"abstract":"Visual representation of the law and legal process can aid in recall and discussion of complicated legal concepts, yet is a skill rarely taught in law schools. This work investigates the use of flowcharts and similar process-oriented diagrams in contemporary legal literature through a literature review and concept-based content analysis. Information visualisations (infovis) identified in the literature are classified into 11 described archetypal diagram types, and the results describe their usage quantitatively by type, year, publication venue and legal domain. We found that the use of infovis in legal literature is extremely rare, identifying not more than 10 articles in each calendar year. We also identified that the concept flow diagram is most commonly used, and that Unified Modelling Language (UML) is the most frequently applied representational approach. This work posits a number of serious questions for legal educators and practicing lawyers regarding how infovis in legal education and practice may improve access to justice, legal education and lay comprehension of complex legal frameworks and processes. It concludes by asking how we can expect communities to understand and adhere to laws that have become so complex and verbose as to be incomprehensible even to many of those who are learned in the law?","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/14738716211012608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44450044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GoCrystal: A gamified visual analytics tool for analysis and visualization of atomic configurations and thermodynamic energy models","authors":"Haeyong Chung, Santhosh Nandhakumar, Gopinath Polasani Vasu, Austin Vickers, Eunseok Lee","doi":"10.1177/1473871620925821","DOIUrl":"https://doi.org/10.1177/1473871620925821","url":null,"abstract":"In this article, we present GoCrystal, a new visual analytics tool for analysis and visualization of atomic configurations and thermodynamic energy models. GoCrystal’s primary objective is to support the visual analytics tasks for finding and understanding favorable atomic patterns in a lattice using gamification. We believe the performance of visual analytics tasks can be improved by employing gamification features. Careful research was conducted in an effort to determine which gamification features would be more applicable for analyzing and exploring atomic configurations and their associated thermodynamic free energy. In addition, we conducted a user study to determine the effectiveness of GoCrystal and its gamification features in achieving this goal, comparing with a conventional visual analytics model without gamification as a control group. Finally, we report the results of the user study and demonstrate the impact that gamification features have on the performance and time necessary to understand atomic configurations.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871620925821","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48821262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GoCrystal: A gamified visual analytics tool for analysis and visualization of atomic configurations and thermodynamic energy models:","authors":"Haeyong Chung, Santhosh Nandhakumar, Gopinath Polasani Vasu, Austin Vickers, Eunseok Lee","doi":"10.25384/SAGE.C.5068787.V1","DOIUrl":"https://doi.org/10.25384/SAGE.C.5068787.V1","url":null,"abstract":"In this article, we present GoCrystal, a new visual analytics tool for analysis and visualization of atomic configurations and thermodynamic energy models. GoCrystal’s primary objective is to suppo...","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46037699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative user study of visualization techniques for cluster analysis of multidimensional data sets","authors":"E. Ventocilla, M. Riveiro","doi":"10.1177/1473871620922166","DOIUrl":"https://doi.org/10.1177/1473871620922166","url":null,"abstract":"This article presents an empirical user study that compares eight multidimensional projection techniques for supporting the estimation of the number of clusters, k , embedded in six multidimensional data sets. The selection of the techniques was based on their intended design, or use, for visually encoding data structures, that is, neighborhood relations between data points or groups of data points in a data set. Concretely, we study: the difference between the estimates of k as given by participants when using different multidimensional projections; the accuracy of user estimations with respect to the number of labels in the data sets; the perceived usability of each multidimensional projection; whether user estimates disagree with k values given by a set of cluster quality measures; and whether there is a difference between experienced and novice users in terms of estimates and perceived usability. The results show that: dendrograms (from Ward’s hierarchical clustering) are likely to lead to estimates of k that are different from those given with other multidimensional projections, while Star Coordinates and Radial Visualizations are likely to lead to similar estimates; t-Stochastic Neighbor Embedding is likely to lead to estimates which are closer to the number of labels in a data set; cluster quality measures are likely to produce estimates which are different from those given by users using Ward and t-Stochastic Neighbor Embedding; U-Matrices and reachability plots will likely have a low perceived usability; and there is no statistically significant difference between the answers of experienced and novice users. Moreover, as data dimensionality increases, cluster quality measures are likely to produce estimates which are different from those perceived by users using any of the assessed multidimensional projections. It is also apparent that the inherent complexity of a data set, as well as the capability of each visual technique to disclose such complexity, has an influence on the perceived usability.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871620922166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48012844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Documentary narrative visualization: Features and modes of documentary film in narrative visualization","authors":"J. Bradbury, R. Guadagno","doi":"10.1177/1473871620925071","DOIUrl":"https://doi.org/10.1177/1473871620925071","url":null,"abstract":"Documentary narrative visualization is a data visualization approach using the features of documentary film. Researchers in the field of visualization are searching for better methods of constructing narratives from data sets. In this article, we explore the structure and techniques of documentary film and how they apply to the practice of constructing narrative visualization with video. We review the structural aspects of documentary film with examples relevant for narrative visualization. Using six of the highest quality video-based narrative visualizations, we conducted a study of user preferences for three pairs of videos. The video pairs were specifically matched to highlight unique features available in documentary film. Using the preferences expressed by our participants, we performed an empirical study to examine the documentary features most valued by our participants. Our results provide implications about the style and features of documentary film that are most useful in the construction of narrative visualization. Overall, this work provides a clear starting point for the construction of documentary narrative visualization providing content creators with specific techniques that will improve engagement of their content.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871620925071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49386549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive visual analytics tool for multidimensional quantitative and categorical data analysis","authors":"Muhammad Laiq Ur Rahman Shahid, V. Molchanov, J. Mir, Furqan Shaukat, L. Linsen","doi":"10.1177/1473871620908034","DOIUrl":"https://doi.org/10.1177/1473871620908034","url":null,"abstract":"With the advances in science and technology, a rapid growth of multidimensional (multivariate) datasets is observed in different fields. Projection and visualization of such data to a lower dimensional space without losing the data structure is a challenging task. We propose an interactive visual analytics tool that is applied for the combined analysis of multidimensional numerical and categorical data. The tool helps the analyst not only to find the clusters of similar objects but also to identify the important features specific to these clusters. The efficacy of the various functionalities of the tool is examined analyzing epidemiological data to understand the pathogenesis of obstructive sleep apnea. Our approach helps the user to visually analyze the data and get a better understanding of the data. The tool would be a valuable resource for analysts working on numerical and categorical data.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871620908034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45892870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The missing path: Analysing incompleteness in knowledge graphs","authors":"Marie Destandau, Jean-Daniel Fekete","doi":"10.1177/1473871621991539","DOIUrl":"https://doi.org/10.1177/1473871621991539","url":null,"abstract":"Knowledge Graphs (KG) allow to merge and connect heterogeneous data despite their differences; they are incomplete by design. Yet, KG data producers need to ensure the best level of completeness, as far as possible. The difficulty is that they have no means to distinguish cases where incomplete entities could and should be fixed. We present a new visualization tool: The Missing Path, to support them in identifying coherent subsets of entities that can be repaired. It relies on a map, grouping entities according to their incomplete profile. The map is coordinated with histograms and stacked charts to support interactive exploration and analysis; the summary of a subset can be compared with the one of the full collection to reveal its distinctive features. We conduct an iterative design process and evaluation with nine Wikidata contributors. Participants gain insights and find various strategies to identify coherent subsets to be fixed.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871621991539","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45778290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}