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Bae, Juhee
Publications (6 of 6) Show all publications
Bae, J., Falkman, G., Helldin, T. & Riveiro, M. (2019). Visual Data Analysis. In: A. Said, & V. Torra (Ed.), Data science in Practice: (pp. 133-155). Springer
Open this publication in new window or tab >>Visual Data Analysis
2019 (English)In: Data science in Practice / [ed] A. Said, & V. Torra, Springer, 2019, p. 133-155Chapter in book (Refereed)
Abstract [en]

Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:hj:diva-48365 (URN)10.1007/978-3-319-97556-6_8 (DOI)000464719500009 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2020-05-13Bibliographically approved
Bae, J., Ventocilla, E., Riveiro, M. & Torra, V. (2018). On the Visualization of Discrete Non-additive Measures. In: Torra V, Mesiar R, Baets B (Ed.), Aggregation Functions in Theory and in Practice AGOP 2017: . Paper presented at 9th International Summer School on Aggregation Functions (AGOP), Skövde, Sweden, June 19-22, 2017 (pp. 200-210). Springer
Open this publication in new window or tab >>On the Visualization of Discrete Non-additive Measures
2018 (English)In: Aggregation Functions in Theory and in Practice AGOP 2017 / [ed] Torra V, Mesiar R, Baets B, Springer, 2018, p. 200-210Conference paper, Published paper (Refereed)
Abstract [en]

Non-additive measures generalize additive measures, and have been utilized in several applications. They are used to represent different types of uncertainty and also to represent importance in data aggregation. As non-additive measures are set functions, the number of values to be considered grows exponentially. This makes difficult their definition but also their interpretation and understanding. In order to support understability, this paper explores the topic of visualizing discrete non-additive measures using node-link diagram representations.

Place, publisher, year, edition, pages
Springer, 2018
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 581
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:hj:diva-43237 (URN)10.1007/978-3-319-59306-7_21 (DOI)000432811600021 ()2-s2.0-85019989762 (Scopus ID)0;0;miljJAIL (Local ID)978-3-319-59306-7 (ISBN)978-3-319-59305-0 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
9th International Summer School on Aggregation Functions (AGOP), Skövde, Sweden, June 19-22, 2017
Available from: 2018-06-14 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Ventocilla, E., Bae, J., Riveiro, M. & Said, A. (2017). A Billiard Metaphor for Exploring Complex Graphs. In: Marijn Koolen, Jaap Kamps, Toine Bogers, Nick Belkin, Diane Kelly, Emine Yilmaz (Ed.), Second Workshop on Supporting Complex Search Tasks: . Paper presented at Second Workshop on Supporting Complex Search Tasks co-located with the ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2017), Oslo, Norway, March 11, 2017 (pp. 37-40).
Open this publication in new window or tab >>A Billiard Metaphor for Exploring Complex Graphs
2017 (English)In: Second Workshop on Supporting Complex Search Tasks / [ed] Marijn Koolen, Jaap Kamps, Toine Bogers, Nick Belkin, Diane Kelly, Emine Yilmaz, 2017, p. 37-40Conference paper, Published paper (Refereed)
Abstract [en]

Exploring and revealing relations between the elements is a fre-quent task in exploratory analysis and search. Examples includethat of correlations of attributes in complex data sets, or facetedsearch. Common visual representations for such relations are di-rected graphs or correlation matrices. These types of visual encod-ings are often - if not always - fully constructed before being shownto the user. This can be thought of as a top-down approach, whereusers are presented with a full picture for them to interpret andunderstand. Such a way of presenting data could lead to a visualoverload, specially when it results in complex graphs with highdegrees of nodes and edges. We propose a bottom-up alternativecalled Billiard where few elements are presented at rst and fromwhich a user can interactively construct the rest based on whats/he nds of interest. The concept is based on a billiard metaphorwhere a cue ball (node) has an eect on other elements (associatednodes) when stroke against them.

Series
CEUR Workshop Proceedings, E-ISSN 1613-0073 ; 1798
Keywords
Visualization, interaction, correlation
National Category
Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:hj:diva-43238 (URN)2-s2.0-85019592292 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
Second Workshop on Supporting Complex Search Tasks co-located with the ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2017), Oslo, Norway, March 11, 2017
Available from: 2018-02-27 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Bae, J., Ventocilla, E., Riveiro, M., Helldin, T. & Falkman, G. (2017). Evaluating Multi-Attributes on Cause and Effect Relationship Visualization. In: Alexandru Telea, Jose Braz, Lars Linsen (Ed.), Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP. Paper presented at 8th International Conference on Information Visualization Theory and Applications (IVAPP), part of the 12th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), February 27-March 1, 2017, in Porto, Portugal (pp. 64-74). SciTePress
Open this publication in new window or tab >>Evaluating Multi-Attributes on Cause and Effect Relationship Visualization
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2017 (English)In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017): Volumne 3: IVAPP / [ed] Alexandru Telea, Jose Braz, Lars Linsen, SciTePress , 2017, p. 64-74Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents findings about visual representations of cause and effect relationship's direction, strength, and uncertainty based on an online user study. While previous researches focus on accuracy and few attributes, our empirical user study examines accuracy and the subjective ratings on three different attributes of a cause and effect relationship edge. The cause and effect direction was depicted by arrows and tapered lines; causal strength by hue, width, and a numeric value; and certainty by granularity, brightness, fuzziness, and a numeric value. Our findings point out that both arrows and tapered cues work well to represent causal direction. Depictions with width showed higher conjunct accuracy and were more preferred than that with hue. Depictions with brightness and fuzziness showed higher accuracy and were marked more understandable than granularity. In general, depictions with hue and granularity performed less accurately and were not preferred compared to the ones with numbers or with width and brightness.

Place, publisher, year, edition, pages
SciTePress, 2017
Keywords
Cause and effect, uncertainty, evaluation, graph visualization
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:hj:diva-43243 (URN)10.5220/0006102300640074 (DOI)000444939500005 ()2-s2.0-85040593124 (Scopus ID)0;0;miljJAIL (Local ID)978-989-758-228-8 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
8th International Conference on Information Visualization Theory and Applications (IVAPP), part of the 12th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), February 27-March 1, 2017, in Porto, Portugal
Funder
Knowledge Foundation
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Bae, J., Helldin, T. & Riveiro, M. (2017). Identifying Root Cause and Derived Effects in Causal Relationships. In: Sakae Yamamoto (Ed.), Human Interface and the Management of Information: Information, Knowledge and Interaction Design: 19th International Conference, HCI International 2017, Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part I. Paper presented at Thematic track on Human Interface and the Management of Information, held as part of the 19th International Conference on Human–Computer Interaction, HCI International 2017, Vancouver, Canada, 9 July 2017 through 14 July 2017 (pp. 22-34). Springer
Open this publication in new window or tab >>Identifying Root Cause and Derived Effects in Causal Relationships
2017 (English)In: Human Interface and the Management of Information: Information, Knowledge and Interaction Design: 19th International Conference, HCI International 2017, Vancouver, BC, Canada, July 9–14, 2017, Proceedings, Part I / [ed] Sakae Yamamoto, Springer , 2017, p. 22-34Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on identifying factors that influence the process of finding a root cause and a derived effect in causal node-link graphs with associated strength and significance depictions. We discuss in detail the factors that seem to be involved in identifying a global cause and effect based on the analysis of the results of an online user study with 44 participants, who used both sequential and non-sequential graph layouts. In summary, the results show that participants show geodesic-path tendencies when selecting causes and derived effects, and that context matters, i.e., participant’s own beliefs, experiences and knowledge might influence graph interpretation.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10273
Keywords
cause and effect, strenght and significance, graph visualization, user study
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:hj:diva-43245 (URN)10.1007/978-3-319-58521-5_2 (DOI)2-s2.0-85025150109 (Scopus ID)0;0;miljJAIL (Local ID)978-3-319-58520-8 (ISBN)978-3-319-58521-5 (ISBN)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
Thematic track on Human Interface and the Management of Information, held as part of the 19th International Conference on Human–Computer Interaction, HCI International 2017, Vancouver, Canada, 9 July 2017 through 14 July 2017
Projects
BIDAF
Funder
Knowledge Foundation
Available from: 2017-10-02 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
Bae, J., Helldin, T. & Riveiro, M. (2017). Understanding Indirect Causal Relationships in Node-Link Graphs. Paper presented at 19th Eurographics/IEEE VGTC Conference on Visualization (EuroVis), JUN 12-16, 2017, Barcelona, SPAIN. Computer graphics forum (Print), 36(3), 411-421
Open this publication in new window or tab >>Understanding Indirect Causal Relationships in Node-Link Graphs
2017 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 36, no 3, p. 411-421Article in journal (Refereed) Published
Abstract [en]

To find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node-link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi-attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.

National Category
Human Computer Interaction Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:hj:diva-43246 (URN)10.1111/cgf.13198 (DOI)000404881200038 ()2-s2.0-85022207775 (Scopus ID)0;0;miljJAIL (Local ID)0;0;miljJAIL (Archive number)0;0;miljJAIL (OAI)
Conference
19th Eurographics/IEEE VGTC Conference on Visualization (EuroVis), JUN 12-16, 2017, Barcelona, SPAIN
Available from: 2017-08-10 Created: 2019-03-05 Last updated: 2019-08-23Bibliographically approved
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