Pub Date : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465259
H. Bhatia, A. Gyulassy, Valerio Pascucci, Martina Bremer, M. Ong, V. Lordi, E. Draeger, J. Pask, P. Bremer
Exploration of atomic trajectories is fundamental to understanding and characterizing complex chemical systems important in many applications. For instance, any new insight into the mechanisms of ionic migration in catalytic materials could lead to a substantial increase in battery performance. A new statistical measure, called the relative-angle distribution, has been proposed to understand complex motion - whether Brownian, ballistic, or diffusive. The relative-angle distribution can be represented as a collection of 1D histograms, but is currently created in a slow, offline process, making any parameter exploration a tedious and time-consuming task. Furthermore, the resulting plot can hide uncertainty in both the data and the visualization. As a result, once rastered or printed at a fixed resolution, these histograms can be misleading. We present a new analysis tool for the exploration of atomic trajectories that combines an interactive histogram visualization with uncertainty information for both data and plotting errors, and is also linked to an interactive 3D display of trajectories. Our tool enables a holistic exploration of trajectories previously not feasible, with the potential for significant scientific impact. In collaboration with domain experts, we have deployed our tool ta analyze molecular dynamics simulations of lithium-ion diffusion. Users have found that the tool significantly accelerates the exploration process and have used it to validate a number of previously unconfirmed hypotheses.
{"title":"Interactive exploration of atomic trajectories through relative-angle distribution and associated uncertainties","authors":"H. Bhatia, A. Gyulassy, Valerio Pascucci, Martina Bremer, M. Ong, V. Lordi, E. Draeger, J. Pask, P. Bremer","doi":"10.1109/PACIFICVIS.2016.7465259","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465259","url":null,"abstract":"Exploration of atomic trajectories is fundamental to understanding and characterizing complex chemical systems important in many applications. For instance, any new insight into the mechanisms of ionic migration in catalytic materials could lead to a substantial increase in battery performance. A new statistical measure, called the relative-angle distribution, has been proposed to understand complex motion - whether Brownian, ballistic, or diffusive. The relative-angle distribution can be represented as a collection of 1D histograms, but is currently created in a slow, offline process, making any parameter exploration a tedious and time-consuming task. Furthermore, the resulting plot can hide uncertainty in both the data and the visualization. As a result, once rastered or printed at a fixed resolution, these histograms can be misleading. We present a new analysis tool for the exploration of atomic trajectories that combines an interactive histogram visualization with uncertainty information for both data and plotting errors, and is also linked to an interactive 3D display of trajectories. Our tool enables a holistic exploration of trajectories previously not feasible, with the potential for significant scientific impact. In collaboration with domain experts, we have deployed our tool ta analyze molecular dynamics simulations of lithium-ion diffusion. Users have found that the tool significantly accelerates the exploration process and have used it to validate a number of previously unconfirmed hypotheses.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115715454","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}
Pub Date : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465278
Jin Xu, Y. Tao, Hai Lin
Word clouds have been widely used to present the contents and themes in the text for summary and visualization. In this paper, we propose a new semantic word cloud taking into account the word semantic meanings. Distributed word representation is applied to accurately describe the semantic meaning of words, and a word similarity graph is constructed based on the semantic distance between words to lay out words in a more compact and aesthetic manner. Word-related interactions are introduced to guide users fast read and understand the text. We apply the proposed word cloud to user generated reviews in different fields to demonstrate the effectiveness of our method.
{"title":"Semantic word cloud generation based on word embeddings","authors":"Jin Xu, Y. Tao, Hai Lin","doi":"10.1109/PACIFICVIS.2016.7465278","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465278","url":null,"abstract":"Word clouds have been widely used to present the contents and themes in the text for summary and visualization. In this paper, we propose a new semantic word cloud taking into account the word semantic meanings. Distributed word representation is applied to accurately describe the semantic meaning of words, and a word similarity graph is constructed based on the semantic distance between words to lay out words in a more compact and aesthetic manner. Word-related interactions are introduced to guide users fast read and understand the text. We apply the proposed word cloud to user generated reviews in different fields to demonstrate the effectiveness of our method.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115790501","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}
Pub Date : 2016-04-19DOI: 10.1109/pacificvis.2016.7465262
K. Buchin, D. Creemers, Andrea Lazzarotto, B. Speckmann, J. Wulms
Word clouds are a popular method to visualize the frequency of words in textual data. Nowadays many text-based data sets, such as Flickr tags, are geo-referenced, that is, they have an important spatial component. However, existing automated methods to generate word clouds are unable to incorporate such spatial information. We introduce geo word clouds: word clouds which capture not only the frequency but also the spatial relevance of words. Our input is a set of locations from one (or more) geographic regions with (possibly several) text labels per location. We aggregate word frequencies according to point clusters and employ a greedy strategy to place appropriately sized labels without overlap as close as possible to their corresponding locations. While doing so we "draw" the spatial shapes of the geographic regions with the corresponding labels. We experimentally explore trade-offs concerning the location of labels, their relative sizes and the number of spatial clusters. The resulting word clouds are visually pleasing and have a low error in terms of relative scaling and locational accuracy of words, while using a small number of clusters per label.
{"title":"Geo word clouds","authors":"K. Buchin, D. Creemers, Andrea Lazzarotto, B. Speckmann, J. Wulms","doi":"10.1109/pacificvis.2016.7465262","DOIUrl":"https://doi.org/10.1109/pacificvis.2016.7465262","url":null,"abstract":"Word clouds are a popular method to visualize the frequency of words in textual data. Nowadays many text-based data sets, such as Flickr tags, are geo-referenced, that is, they have an important spatial component. However, existing automated methods to generate word clouds are unable to incorporate such spatial information. We introduce geo word clouds: word clouds which capture not only the frequency but also the spatial relevance of words. Our input is a set of locations from one (or more) geographic regions with (possibly several) text labels per location. We aggregate word frequencies according to point clusters and employ a greedy strategy to place appropriately sized labels without overlap as close as possible to their corresponding locations. While doing so we \"draw\" the spatial shapes of the geographic regions with the corresponding labels. We experimentally explore trade-offs concerning the location of labels, their relative sizes and the number of spatial clusters. The resulting word clouds are visually pleasing and have a low error in terms of relative scaling and locational accuracy of words, while using a small number of clusters per label.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122440876","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}
Pub Date : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465244
Michaël Aupetit, M. Sedlmair
Our goal is to accurately model human class separation judgements in color-coded scatterplots. Towards this goal, we propose a set of 2002 visual separation measures, by systematically combining 17 neighborhood graphs and 14 class purity functions, with different parameterizations. Using a Machine Learning framework, we evaluate these measures based on how well they predict human separation judgements. We found that more than 58% of the 2002 new measures outperform the best state-of-the-art Distance Consistency (DSC) measure. Among the 2002, the best measure is the average proportion of same-class neighbors among the 0.35-Observable Neighbors of each point of the target class (short GONG 0.35 DIR CPT), with a prediction accuracy of 92.9%, which is 11.7% better than DSC. We also discuss alternative, well-performing measures and give guidelines when to use which.
我们的目标是在颜色编码的散点图中准确地模拟人类的阶级分离判断。为了实现这一目标，我们通过系统地组合17个邻域图和14个不同参数化的类纯度函数，提出了一套2002年的视觉分离措施。使用机器学习框架，我们根据它们预测人类分离判断的程度来评估这些措施。我们发现超过58%的2002年新测量优于最先进的距离一致性(DSC)测量。其中，最好的度量是目标类各点的0.35个可观测邻居中同类邻居的平均比例(简称GONG 0.35 DIR CPT)，预测精度为92.9%，比DSC提高11.7%。我们还讨论了可选的、执行良好的度量方法，并给出了何时使用哪一种方法的指导方针。
{"title":"SepMe: 2002 New visual separation measures","authors":"Michaël Aupetit, M. Sedlmair","doi":"10.1109/PACIFICVIS.2016.7465244","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465244","url":null,"abstract":"Our goal is to accurately model human class separation judgements in color-coded scatterplots. Towards this goal, we propose a set of 2002 visual separation measures, by systematically combining 17 neighborhood graphs and 14 class purity functions, with different parameterizations. Using a Machine Learning framework, we evaluate these measures based on how well they predict human separation judgements. We found that more than 58% of the 2002 new measures outperform the best state-of-the-art Distance Consistency (DSC) measure. Among the 2002, the best measure is the average proportion of same-class neighbors among the 0.35-Observable Neighbors of each point of the target class (short GONG 0.35 DIR CPT), with a prediction accuracy of 92.9%, which is 11.7% better than DSC. We also discuss alternative, well-performing measures and give guidelines when to use which.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125374189","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}
Pub Date : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465280
Hsin-I Chen, Wei-Ting Lin, Bing-Yu Chen
Online bookstores have highly thrived and changed consumer behaviors in these years. However, most customers go to online bookstores only when they have specific targets. One reason is that the current web interfaces are usually too complex and cluttered for users to browse. In addition, current visualization interfaces only display the results associated with a single attribute, thus requiring users to interact intensively to find their targets. Inspired by the user experiences (UX) in physical bookstores, we present Bookwall, an online bookstore interface which comprises two components: Category Map and Wall View, enabling users to find their targets more efficiently and releasing users from the burden of complicated operations. Specifically, the category map produces a map with a "natural" map-like look, providing an overview of the clusters and neighborhood of book categories. The wall view enables displaying query results satisfying dual query attributes simultaneously. The results show that Bookwall can provide the users a favourable alternative visualization.
{"title":"Bookwall: Visualizing books online based on user experience in physical bookstores","authors":"Hsin-I Chen, Wei-Ting Lin, Bing-Yu Chen","doi":"10.1109/PACIFICVIS.2016.7465280","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465280","url":null,"abstract":"Online bookstores have highly thrived and changed consumer behaviors in these years. However, most customers go to online bookstores only when they have specific targets. One reason is that the current web interfaces are usually too complex and cluttered for users to browse. In addition, current visualization interfaces only display the results associated with a single attribute, thus requiring users to interact intensively to find their targets. Inspired by the user experiences (UX) in physical bookstores, we present Bookwall, an online bookstore interface which comprises two components: Category Map and Wall View, enabling users to find their targets more efficiently and releasing users from the burden of complicated operations. Specifically, the category map produces a map with a \"natural\" map-like look, providing an overview of the clusters and neighborhood of book categories. The wall view enables displaying query results satisfying dual query attributes simultaneously. The results show that Bookwall can provide the users a favourable alternative visualization.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116992497","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}
Pub Date : 2016-04-01DOI: 10.1109/PACIFICVIS.2016.7465252
D. Maljovec, Bei Wang, P. Rosen, A. Alfonsi, G. Pastore, C. Rabiti, Valerio Pascucci
In nuclear engineering, understanding the safety margins of the nuclear reactor via simulations is arguably of paramount importance in predicting and preventing nuclear accidents. It is therefore crucial to perform sensitivity analysis to understand how changes in the model inputs affect the outputs. Modern nuclear simulation tools rely on numerical representations of the sensitivity information - inherently lacking in visual encodings - offering limited effectiveness in communicating and exploring the generated data. In this paper, we design a framework for sensitivity analysis and visualization of multidimensional nuclear simulation data using partition-based, topology-inspired regression models and report on its efficacy. We rely on the established Morse-Smale regression technique, which allows us to partition the domain into monotonic regions where easily interpretable linear models can be used to assess the influence of inputs on the output variability. The underlying computation is augmented with an intuitive and interactive visual design to effectively communicate sensitivity information to nuclear scientists. Our framework is being deployed into the multipurpose probabilistic risk assessment and uncertainty quantification framework RAVEN (Reactor Analysis and Virtual Control Environment). We evaluate our framework using a simulation dataset studying nuclear fuel performance.
{"title":"Rethinking sensitivity analysis of nuclear simulations with topology","authors":"D. Maljovec, Bei Wang, P. Rosen, A. Alfonsi, G. Pastore, C. Rabiti, Valerio Pascucci","doi":"10.1109/PACIFICVIS.2016.7465252","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465252","url":null,"abstract":"In nuclear engineering, understanding the safety margins of the nuclear reactor via simulations is arguably of paramount importance in predicting and preventing nuclear accidents. It is therefore crucial to perform sensitivity analysis to understand how changes in the model inputs affect the outputs. Modern nuclear simulation tools rely on numerical representations of the sensitivity information - inherently lacking in visual encodings - offering limited effectiveness in communicating and exploring the generated data. In this paper, we design a framework for sensitivity analysis and visualization of multidimensional nuclear simulation data using partition-based, topology-inspired regression models and report on its efficacy. We rely on the established Morse-Smale regression technique, which allows us to partition the domain into monotonic regions where easily interpretable linear models can be used to assess the influence of inputs on the output variability. The underlying computation is augmented with an intuitive and interactive visual design to effectively communicate sensitivity information to nuclear scientists. Our framework is being deployed into the multipurpose probabilistic risk assessment and uncertainty quantification framework RAVEN (Reactor Analysis and Virtual Control Environment). We evaluate our framework using a simulation dataset studying nuclear fuel performance.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129867346","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}
Pub Date : 2016-04-01DOI: 10.1109/PACIFICVIS.2016.7465269
Tongshuang Sherry Wu, Y. Yao, Y. Duan, Xinzhi Fan, Huamin Qu
The rising trend of MOOCs has attracted wide ranging research interests. Among all the existing studies related to MOOCs, most of them focus on individuals' study behaviors and evaluations (e.g., analysis on click streams for video-watching behavior exploration, etc.) for course design purposes. However, in addition to traditional course materials, MOOCs also provide interactive user forums to encourage students to seek help from peers, which endows the courses with social network formation and interaction. Thus, we present NetworkSeer to help evaluate why MOOC students use forums, and what they do. NetworkSeer visualizes interactions in the forum, including where, when the interactions happen, and why. It also enables filtering out un-targeted groups. A case study is conducted to demonstrate its usefulness.
{"title":"NetworkSeer: Visual analysis for social network in MOOCs","authors":"Tongshuang Sherry Wu, Y. Yao, Y. Duan, Xinzhi Fan, Huamin Qu","doi":"10.1109/PACIFICVIS.2016.7465269","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465269","url":null,"abstract":"The rising trend of MOOCs has attracted wide ranging research interests. Among all the existing studies related to MOOCs, most of them focus on individuals' study behaviors and evaluations (e.g., analysis on click streams for video-watching behavior exploration, etc.) for course design purposes. However, in addition to traditional course materials, MOOCs also provide interactive user forums to encourage students to seek help from peers, which endows the courses with social network formation and interaction. Thus, we present NetworkSeer to help evaluate why MOOC students use forums, and what they do. NetworkSeer visualizes interactions in the forum, including where, when the interactions happen, and why. It also enables filtering out un-targeted groups. A case study is conducted to demonstrate its usefulness.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131681800","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}
Stacked graphs have been widely used to represent multiple time series simultaneously to show the changes of individual values and their aggregation over time. However, when the number of time series becomes very large, the layers representing time series with small values take up only very small proportions in the stacked graph, making them hard to trace. As a result, it is challenging for analysts to detect the correlation of individual layers and their aggregation, and find trend similarities and differences between layers solely with stacked graphs. In this paper, we study the correlations of individual layers, and their aggregation in time series data presented with stacked graphs, focusing on the local regions within any given time intervals. Specifically, we present STAC, an interactive visual analytics system, to help analysts gain insights into the correlations in stacked graphs. While preserving the original stacked shape, we further link a stacked graph with auxiliary views to facilitate the in-depth analysis of correlations in time series data. A case study based on a real-world dataset demonstrates the effectiveness of our system in gaining insights into time series data analysis and facilitating various analytical tasks.
{"title":"STAC: Enhancing stacked graphs for time series analysis","authors":"Yun Wang, Tongshuang Sherry Wu, Zhutian Chen, Qiong Luo, Huamin Qu","doi":"10.1109/PACIFICVIS.2016.7465277","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465277","url":null,"abstract":"Stacked graphs have been widely used to represent multiple time series simultaneously to show the changes of individual values and their aggregation over time. However, when the number of time series becomes very large, the layers representing time series with small values take up only very small proportions in the stacked graph, making them hard to trace. As a result, it is challenging for analysts to detect the correlation of individual layers and their aggregation, and find trend similarities and differences between layers solely with stacked graphs. In this paper, we study the correlations of individual layers, and their aggregation in time series data presented with stacked graphs, focusing on the local regions within any given time intervals. Specifically, we present STAC, an interactive visual analytics system, to help analysts gain insights into the correlations in stacked graphs. While preserving the original stacked shape, we further link a stacked graph with auxiliary views to facilitate the in-depth analysis of correlations in time series data. A case study based on a real-world dataset demonstrates the effectiveness of our system in gaining insights into time series data analysis and facilitating various analytical tasks.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126967467","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}
Pub Date : 2016-04-01DOI: 10.1109/PACIFICVIS.2016.7465264
M. Meyer, Fabian Beck, S. Lohmann
Stored procedures are used in database systems to process and aggregate data. Hundreds of stored procedures often form a complex process network with documented and hidden dependencies that is difficult to understand, maintain, and debug. This paper introduces a novel approach to support such tasks by visually comparing a specific process run to other runs of the same process. The visualization is based on a force-directed node-link diagram arranged on a timeline. Color coding, histograms, and trend charts are used to highlight temporal deviations. The approach has been implemented as an interactive web application and used by professional database developers for solving realistic maintenance and debugging tasks. The feedback of these expert users confirms the usefulness and practical relevance of the approach.
{"title":"Visual monitoring of process runs: An application study for stored procedures","authors":"M. Meyer, Fabian Beck, S. Lohmann","doi":"10.1109/PACIFICVIS.2016.7465264","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465264","url":null,"abstract":"Stored procedures are used in database systems to process and aggregate data. Hundreds of stored procedures often form a complex process network with documented and hidden dependencies that is difficult to understand, maintain, and debug. This paper introduces a novel approach to support such tasks by visually comparing a specific process run to other runs of the same process. The visualization is based on a force-directed node-link diagram arranged on a timeline. Color coding, histograms, and trend charts are used to highlight temporal deviations. The approach has been implemented as an interactive web application and used by professional database developers for solving realistic maintenance and debugging tasks. The feedback of these expert users confirms the usefulness and practical relevance of the approach.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130336774","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}
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