Pub Date : 2021-07-01DOI: 10.1177/14738716211021591
Alessandra Maciel Paz Milani, Lucas Angelo Loges, F. Paulovich, I. Manssour
To accommodate the demands of a data-driven society, we have expanded our ability to collect and store data, develop sophisticated algorithms, and generate elaborated visual representations of the data analysis process outcomes. However, data preprocessing, as the activity of transforming the raw data into an appropriate format for subsequent analysis, is still a challenging part of this process. Although we can find studies that address the use of visualization techniques to support the activities in the scope of preprocessing, the current Visual Analytics processes do not consider preprocessing an equally important phase in their processes. Hence, with this paper, we aim to contribute to the discussion of how we can incorporate the preprocessing as a prominent phase in the Visual Analytics process and promote better alternatives to assist the data analysts during the preprocessing activities. To achieve that, we are introducing the Preprocessing Profiling Approach for Visual Analytics (PrAVA), a conceptual Visual Analytics process that includes Preprocessing Profiling as a new phase. It also contemplates a set of guidelines to be considered by new solutions adopting PrAVA. Moreover, we analyze its applicability through use case scenarios that show resourceful methods for data understanding and evaluation of the preprocessing impacts. As a final contribution, we indicate a list of research opportunities in the scope of preprocessing combined with visualization and Visual Analytics to stimulate a shift to visual preprocessing.
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Pub Date : 2021-07-01DOI: 10.1177/14738716211028136
Alihan Okka, U. Dogrusoz, Hasan Balci
This paper describes a new automatic layout algorithm named CoSEP for compound graphs with port constraints. The algorithm works by extending the physical model of a previous algorithm named CoSE by defining additional force types and heuristics for constraining edges to connect to certain user-defined locations on end nodes. Similar to its predecessor, CoSEP also accounts for non-uniform node dimensions and arbitrary levels of nesting via compound nodes. Our experiments show that CoSEP significantly improves the quality of the layouts for compound graphs with port constraints with respect to commonly accepted graph drawing criteria while running reasonably fast, suitable for use in interactive applications for small to medium-sized (up to 500 nodes) graphs. A complete JavaScript implementation of CoSEP as a Cytoscape.js extension along with a demo page is freely available at https://github.com/iVis-at-Bilkent/cytoscape.js-cosep.
{"title":"CoSEP: A compound spring embedder layout algorithm with support for ports","authors":"Alihan Okka, U. Dogrusoz, Hasan Balci","doi":"10.1177/14738716211028136","DOIUrl":"https://doi.org/10.1177/14738716211028136","url":null,"abstract":"This paper describes a new automatic layout algorithm named CoSEP for compound graphs with port constraints. The algorithm works by extending the physical model of a previous algorithm named CoSE by defining additional force types and heuristics for constraining edges to connect to certain user-defined locations on end nodes. Similar to its predecessor, CoSEP also accounts for non-uniform node dimensions and arbitrary levels of nesting via compound nodes. Our experiments show that CoSEP significantly improves the quality of the layouts for compound graphs with port constraints with respect to commonly accepted graph drawing criteria while running reasonably fast, suitable for use in interactive applications for small to medium-sized (up to 500 nodes) graphs. A complete JavaScript implementation of CoSEP as a Cytoscape.js extension along with a demo page is freely available at https://github.com/iVis-at-Bilkent/cytoscape.js-cosep.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":"20 1","pages":"151 - 169"},"PeriodicalIF":2.3,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/14738716211028136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48391743","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 : 2021-01-01DOI: 10.1177/1473871620980121
A. Friedman
Scholars in scientific disciplines face unique challenges in the creation of visualizations, especially in publications that require insights derived from analyses to be visually displayed. The literature on visualizations describes different techniques and best practices for the creation of graphs. However, these techniques have not been used to evaluate the impact of visualizations in academic publications. In the field of ecology, as in other scientific fields, graphs are an essential part of journal articles. Little is known about the connections between the kind of data presented and domain in which the researchers conducted their study that together produces the visual graphics. This study focused on articles published in the Journal of Ecology between 1996 and 2016 to explore possible connections between data type, domain, and visualization type. Specifically, this study asked three questions: How many of the graphics published between 1996 and 2016 follow a particular set of recommendations for best practices? What can Pearson correlations reveal about the relationships between type of data, domain of study, and visual displays? Can the findings be examined through an inter-reliability test lens? Out of the 20,080 visualizations assessed, 54% included unnecessary graphical elements in the early part of the study (1996–2010). The most common type of data was univariate (35%) and it was often displayed using line graphs. Twenty-one percent of the articles in the period studied could be categorized under the domain type “single species.” Pearson correlation analysis showed that data type and domain type was positively correlated (r = 0.08; p ≤ 0.05). Cohen’s kappa for the reliability test was 0.86, suggesting good agreement between the two categories. This study provides evidence that data type and domain types are equally important in determining the type of visualizations found in scientific journals.
{"title":"Data and visual displays in the Journal of Ecology 1996–2016","authors":"A. Friedman","doi":"10.1177/1473871620980121","DOIUrl":"https://doi.org/10.1177/1473871620980121","url":null,"abstract":"Scholars in scientific disciplines face unique challenges in the creation of visualizations, especially in publications that require insights derived from analyses to be visually displayed. The literature on visualizations describes different techniques and best practices for the creation of graphs. However, these techniques have not been used to evaluate the impact of visualizations in academic publications. In the field of ecology, as in other scientific fields, graphs are an essential part of journal articles. Little is known about the connections between the kind of data presented and domain in which the researchers conducted their study that together produces the visual graphics. This study focused on articles published in the Journal of Ecology between 1996 and 2016 to explore possible connections between data type, domain, and visualization type. Specifically, this study asked three questions: How many of the graphics published between 1996 and 2016 follow a particular set of recommendations for best practices? What can Pearson correlations reveal about the relationships between type of data, domain of study, and visual displays? Can the findings be examined through an inter-reliability test lens? Out of the 20,080 visualizations assessed, 54% included unnecessary graphical elements in the early part of the study (1996–2010). The most common type of data was univariate (35%) and it was often displayed using line graphs. Twenty-one percent of the articles in the period studied could be categorized under the domain type “single species.” Pearson correlation analysis showed that data type and domain type was positively correlated (r = 0.08; p ≤ 0.05). Cohen’s kappa for the reliability test was 0.86, suggesting good agreement between the two categories. This study provides evidence that data type and domain types are equally important in determining the type of visualizations found in scientific journals.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":"20 1","pages":"35 - 46"},"PeriodicalIF":2.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1473871620980121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43246860","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 : 2021-01-01DOI: 10.1016/B978-0-12-381464-7.00009-0
C. Ware
{"title":"Images, Narrative, and Gestures for Explanation","authors":"C. Ware","doi":"10.1016/B978-0-12-381464-7.00009-0","DOIUrl":"https://doi.org/10.1016/B978-0-12-381464-7.00009-0","url":null,"abstract":"","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54062702","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 : 2021-01-01DOI: 10.1016/B978-0-12-381464-7.00008-9
C. Ware
{"title":"Visual Objects and Data Objects","authors":"C. Ware","doi":"10.1016/B978-0-12-381464-7.00008-9","DOIUrl":"https://doi.org/10.1016/B978-0-12-381464-7.00008-9","url":null,"abstract":"","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54062567","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 : 2021-01-01DOI: 10.1016/B978-0-12-381464-7.00001-6
C. Ware
{"title":"Foundations for an Applied Science of Data Visualization","authors":"C. Ware","doi":"10.1016/B978-0-12-381464-7.00001-6","DOIUrl":"https://doi.org/10.1016/B978-0-12-381464-7.00001-6","url":null,"abstract":"","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/B978-0-12-381464-7.00001-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54062494","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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