Pub Date : 2021-01-01DOI: 10.1177/1473871620986249
Manuela Waldner, Thomas Geymayer, D. Schmalstieg, M. Sedlmair
Many professionals, like journalists, writers, or consultants, need to acquire information from various sources, make sense of this unstructured evidence, structure their observations, and finally create and deliver their product, such as a report or a presentation. In formative interviews, we found that tools allowing structuring of observations are often disconnected from the corresponding evidence. Therefore, we designed a sensemaking environment with a flexible observation graph that visually ties together evidence in unstructured documents with the user’s structured knowledge. This is achieved through bi-directional deep links between highlighted document portions and nodes in the observation graph. In a controlled study, we compared users’ sensemaking strategies using either the observation graph or a simple text editor on a large display. Results show that the observation graph represents a holistic, compact representation of users’ observations, which can be linked to unstructured evidence on demand. In contrast, users taking textual notes required much more display space to spatially organize source documents containing unstructured evidence. This implies that spatial organization is a powerful strategy to structure observations even if the available space is limited.
{"title":"Linking unstructured evidence to structured observations","authors":"Manuela Waldner, Thomas Geymayer, D. Schmalstieg, M. Sedlmair","doi":"10.1177/1473871620986249","DOIUrl":"https://doi.org/10.1177/1473871620986249","url":null,"abstract":"Many professionals, like journalists, writers, or consultants, need to acquire information from various sources, make sense of this unstructured evidence, structure their observations, and finally create and deliver their product, such as a report or a presentation. In formative interviews, we found that tools allowing structuring of observations are often disconnected from the corresponding evidence. Therefore, we designed a sensemaking environment with a flexible observation graph that visually ties together evidence in unstructured documents with the user’s structured knowledge. This is achieved through bi-directional deep links between highlighted document portions and nodes in the observation graph. In a controlled study, we compared users’ sensemaking strategies using either the observation graph or a simple text editor on a large display. Results show that the observation graph represents a holistic, compact representation of users’ observations, which can be linked to unstructured evidence on demand. In contrast, users taking textual notes required much more display space to spatially organize source documents containing unstructured evidence. This implies that spatial organization is a powerful strategy to structure observations even if the available space is limited.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":"20 1","pages":"47 - 65"},"PeriodicalIF":2.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871620986249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49622038","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}
Pub Date : 2020-12-08DOI: 10.1177/1473871620978209
Haili Zhang, Pu Wang, Xuejin Gao, Yongsheng Qi, Huihui Gao
T-distributed stochastic neighbor embedding (t-SNE) is an effective visualization method. However, it is non-parametric and cannot be applied to steaming data or online scenarios. Although kernel t-SNE provides an explicit projection from a high-dimensional data space to a low-dimensional feature space, some outliers are not well projected. In this paper, bi-kernel t-SNE is proposed for out-of-sample data visualization. Gaussian kernel matrices of the input and feature spaces are used to approximate the explicit projection. Then principal component analysis is applied to reduce the dimensionality of the feature kernel matrix. Thus, the difference between inliers and outliers is revealed. And any new sample can be well mapped. The performance of the proposed method for out-of-sample projection is tested on several benchmark datasets by comparing it with other state-of-the-art algorithms.
{"title":"Out-of-sample data visualization using bi-kernel t-SNE","authors":"Haili Zhang, Pu Wang, Xuejin Gao, Yongsheng Qi, Huihui Gao","doi":"10.1177/1473871620978209","DOIUrl":"https://doi.org/10.1177/1473871620978209","url":null,"abstract":"T-distributed stochastic neighbor embedding (t-SNE) is an effective visualization method. However, it is non-parametric and cannot be applied to steaming data or online scenarios. Although kernel t-SNE provides an explicit projection from a high-dimensional data space to a low-dimensional feature space, some outliers are not well projected. In this paper, bi-kernel t-SNE is proposed for out-of-sample data visualization. Gaussian kernel matrices of the input and feature spaces are used to approximate the explicit projection. Then principal component analysis is applied to reduce the dimensionality of the feature kernel matrix. Thus, the difference between inliers and outliers is revealed. And any new sample can be well mapped. The performance of the proposed method for out-of-sample projection is tested on several benchmark datasets by comparing it with other state-of-the-art algorithms.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":"20 1","pages":"20 - 34"},"PeriodicalIF":2.3,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871620978209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44612425","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}
Pub Date : 2020-11-24DOI: 10.1177/1473871620972339
E. D. Giacomo, W. Didimo, M. Kaufmann, G. Liotta
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.
{"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":"20 1","pages":"3 - 19"},"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}
Pub Date : 2020-11-08DOI: 10.1177/1473871620966638
Úrsula Torres-Parejo, Jesús R. Campaña, M. Vila, M. Delgado
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.
{"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":"20 1","pages":"83 - 97"},"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}
Pub Date : 2020-11-03DOI: 10.1177/14738716211028565
Haihan Lin, R. Metcalf, Jack T. Wilburn, A. Lex
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.
{"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":"20 1","pages":"123 - 137"},"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}
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