Pub Date : 2020-11-01DOI: 10.1177/14738716211012608
S. McLachlan, L. Webley
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?
{"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":"20 1","pages":"192 - 204"},"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}
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.
{"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":"19 1","pages":"296 - 317"},"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}
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...
{"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":"19 1","pages":"296-317"},"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}
Pub Date : 2020-07-04DOI: 10.1177/1473871620922166
E. Ventocilla, M. Riveiro
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.
{"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":"19 1","pages":"318 - 338"},"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}
Pub Date : 2020-07-03DOI: 10.1177/1473871620925071
J. Bradbury, R. Guadagno
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.
{"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":"19 1","pages":"339 - 352"},"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}
Pub Date : 2020-05-25DOI: 10.1177/1473871620908034
Muhammad Laiq Ur Rahman Shahid, V. Molchanov, J. Mir, Furqan Shaukat, L. Linsen
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.
{"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":"19 1","pages":"234 - 246"},"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}
Pub Date : 2020-05-16DOI: 10.1177/1473871621991539
Marie Destandau, Jean-Daniel Fekete
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.
{"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":"20 1","pages":"66 - 82"},"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}
Pub Date : 2020-04-01DOI: 10.1177/1473871619881114
B. d'Auriol
The position that visualization is an intimate part of human existence and associated with the human species is advanced in this work: visualization abounds delimited by the space of individuality across human history. Visualization involves two complementary aspects of the uniqueness deemed of individuals: individualization reflects individuals’ capabilities and personalization reflects designs that seek compatibility with individuals’ capabilities. This has a number of implications upon the design and evaluation of visualizations. For one, a suitable visualization model that expresses individualization and personalization is needed: a brief survey of models is presented. For another, addressing intellectual uniqueness requires deep analysis and selective objective balance due to the potentially humongous number of unique ideas that support visualization design and viewer experiences. The Engineering Insightful Serviceable Visualizations model is selected as a guide for a comprehensive visualization evaluation of Albrecht Dürer’s 1515 celestial charts. Motivating this choice of visualization is its significance as the first notable and influential European star chart intended for scientific use and mass viewership, and as a blending of science and art. In addition, there is a lack of discussion concerning this particular visualization in the visualization literature. Concluding remarks suggest the significance of approaching visualization from this point-of-view.
{"title":"Open our visualization eyes, individualization: On Albrecht Dürer’s 1515 wood cut celestial charts","authors":"B. d'Auriol","doi":"10.1177/1473871619881114","DOIUrl":"https://doi.org/10.1177/1473871619881114","url":null,"abstract":"The position that visualization is an intimate part of human existence and associated with the human species is advanced in this work: visualization abounds delimited by the space of individuality across human history. Visualization involves two complementary aspects of the uniqueness deemed of individuals: individualization reflects individuals’ capabilities and personalization reflects designs that seek compatibility with individuals’ capabilities. This has a number of implications upon the design and evaluation of visualizations. For one, a suitable visualization model that expresses individualization and personalization is needed: a brief survey of models is presented. For another, addressing intellectual uniqueness requires deep analysis and selective objective balance due to the potentially humongous number of unique ideas that support visualization design and viewer experiences. The Engineering Insightful Serviceable Visualizations model is selected as a guide for a comprehensive visualization evaluation of Albrecht Dürer’s 1515 celestial charts. Motivating this choice of visualization is its significance as the first notable and influential European star chart intended for scientific use and mass viewership, and as a blending of science and art. In addition, there is a lack of discussion concerning this particular visualization in the visualization literature. Concluding remarks suggest the significance of approaching visualization from this point-of-view.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":"19 1","pages":"137 - 162"},"PeriodicalIF":2.3,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871619881114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45053106","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 : 2020-03-19DOI: 10.1177/1473871620904671
Angelos Chatzimparmpas, R. M. Martins, Ilir Jusufi, A. Kerren
Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a meta-analysis (i.e. a “survey of surveys”) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.
{"title":"A survey of surveys on the use of visualization for interpreting machine learning models","authors":"Angelos Chatzimparmpas, R. M. Martins, Ilir Jusufi, A. Kerren","doi":"10.1177/1473871620904671","DOIUrl":"https://doi.org/10.1177/1473871620904671","url":null,"abstract":"Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a meta-analysis (i.e. a “survey of surveys”) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":"19 1","pages":"207 - 233"},"PeriodicalIF":2.3,"publicationDate":"2020-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871620904671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45324813","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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