Pub Date : 1900-01-01DOI: 10.5220/0005292801950206
J. Fuchs, Dominik Jäckle, Niklas Weiler, T. Schreck
In exploratory data analysis, important analysis tasks include the assessment of similarity of data points, labeling of outliers, identifying and relating groups in data, and more generally, the detection of patterns. Specifically, for large data sets, such tasks may be effectively addressed by glyph-based visualizations. Appropriately defined glyph designs and layouts may represent collections of data to address these aforementioned tasks. Important problems in glyph visualization include the design of compact glyph representations, and a similarityor structure-preserving 2D layout. Projection-based techniques are commonly used to generate layouts, but often suffer from over-plotting in 2D display space, which may hinder comparing and relating tasks. We introduce a novel glyph design for visualizing multi-dimensional data based on an environmental metaphor. Motivated by the humans ability to visually discriminate natural shapes like trees in a forest, single flowers in a flower-bed, or leaves at shrubs, we design a leaf-shaped data glyph, where data controls main leaf properties including leaf morphology, leaf venation, and leaf boundary shape. We also define a custom visual aggregation scheme to scale the glyph for large numbers of data records. We show by example that our design is effectively interpretable to solve multivariate data analysis tasks, and provides effective data mapping. The design also provides an aesthetically pleasing appearance, which may help spark interest in data visualization by larger audiences, making it applicable e.g., in mass media.
{"title":"Leaf Glyph - Visualizing Multi-dimensional Data with Environmental Cues","authors":"J. Fuchs, Dominik Jäckle, Niklas Weiler, T. Schreck","doi":"10.5220/0005292801950206","DOIUrl":"https://doi.org/10.5220/0005292801950206","url":null,"abstract":"In exploratory data analysis, important analysis tasks include the assessment of similarity of data points, labeling of outliers, identifying and relating groups in data, and more generally, the detection of patterns. Specifically, for large data sets, such tasks may be effectively addressed by glyph-based visualizations. Appropriately defined glyph designs and layouts may represent collections of data to address these aforementioned tasks. Important problems in glyph visualization include the design of compact glyph representations, and a similarityor structure-preserving 2D layout. Projection-based techniques are commonly used to generate layouts, but often suffer from over-plotting in 2D display space, which may hinder comparing and relating tasks. We introduce a novel glyph design for visualizing multi-dimensional data based on an environmental metaphor. Motivated by the humans ability to visually discriminate natural shapes like trees in a forest, single flowers in a flower-bed, or leaves at shrubs, we design a leaf-shaped data glyph, where data controls main leaf properties including leaf morphology, leaf venation, and leaf boundary shape. We also define a custom visual aggregation scheme to scale the glyph for large numbers of data records. We show by example that our design is effectively interpretable to solve multivariate data analysis tasks, and provides effective data mapping. The design also provides an aesthetically pleasing appearance, which may help spark interest in data visualization by larger audiences, making it applicable e.g., in mass media.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115602961","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 : 1900-01-01DOI: 10.5220/0005313400510062
Ronak Etemadpour, L. Linsen, C. Crick, A. Forbes
When investigating multidimensional data sets with very large numbers of objects and/or a very large number of dimensions, a variety of visualization methods can be employed in order to represent the data effectively and to enable the user to explore the data at different levels of detail. A common strategy for encoding multidimensional data for visual analysis is to use dimensionality reduction techniques that project data from higher dimensions onto a lower-dimensional space. In this paper, we focus on projection techniques that output 2D or 3D scatterplots which can then be used for a range of data analysis tasks. Existing taxonomies for multidimensional data projections focus primarily on tasks in order to evaluate the human perception of class or cluster separation and/or preservation. However, real-world data analysis of complex data sets often includes other tasks besides cluster separation, such as: cluster identification, similarity seeking, cluster ranking, comparisons, counting objects, etc. A contribution of this paper is the identification of subtasks grouped into four main categories of data analysis tasks. We believe that this user-centric task categorization can be used to guide the organization of multidimensional data projection layouts. Moreover, this taxonomy can be used as a guideline for visualization designers when faced with complex data sets requiring dimensionality reduction. Our taxonomy aims to help designers evaluate the effectiveness of a visualization system by providing an expanded range of relevant tasks. These tasks are gathered from an extensive study of visual analytics projects across real-world application domains, all of which involve multidimensional projection. In addition to our survey of tasks and the creation of the task taxonomy, we also explore in more detail specific examples of how to represent data sets effectively for particular tasks. These case studies, while not exhaustive, provide a framework for how specifically to reason about tasks and to decide on visualization methods. That is, we believe that this taxonomy will help visualization designers to determine which visualization methods are appropriate for specific multidimensional data projection tasks.
{"title":"A User-centric Taxonomy for Multidimensional Data Projection Tasks","authors":"Ronak Etemadpour, L. Linsen, C. Crick, A. Forbes","doi":"10.5220/0005313400510062","DOIUrl":"https://doi.org/10.5220/0005313400510062","url":null,"abstract":"When investigating multidimensional data sets with very large numbers of objects and/or a very large number of dimensions, a variety of visualization methods can be employed in order to represent the data effectively and to enable the user to explore the data at different levels of detail. A common strategy for encoding multidimensional data for visual analysis is to use dimensionality reduction techniques that project data from higher dimensions onto a lower-dimensional space. In this paper, we focus on projection techniques that output 2D or 3D scatterplots which can then be used for a range of data analysis tasks. Existing taxonomies for multidimensional data projections focus primarily on tasks in order to evaluate the human perception of class or cluster separation and/or preservation. However, real-world data analysis of complex data sets often includes other tasks besides cluster separation, such as: cluster identification, similarity seeking, cluster ranking, comparisons, counting objects, etc. A contribution of this paper is the identification of subtasks grouped into four main categories of data analysis tasks. We believe that this user-centric task categorization can be used to guide the organization of multidimensional data projection layouts. Moreover, this taxonomy can be used as a guideline for visualization designers when faced with complex data sets requiring dimensionality reduction. Our taxonomy aims to help designers evaluate the effectiveness of a visualization system by providing an expanded range of relevant tasks. These tasks are gathered from an extensive study of visual analytics projects across real-world application domains, all of which involve multidimensional projection. In addition to our survey of tasks and the creation of the task taxonomy, we also explore in more detail specific examples of how to represent data sets effectively for particular tasks. These case studies, while not exhaustive, provide a framework for how specifically to reason about tasks and to decide on visualization methods. That is, we believe that this taxonomy will help visualization designers to determine which visualization methods are appropriate for specific multidimensional data projection tasks.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131465363","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 : 1900-01-01DOI: 10.5220/0005268000170028
O. Hoeber, M. Hasan
Collecting multiple geospatial datasets that describe the same real-world events can be useful in monitoring and enforcement situations (e.g., independently tracking where a fishing vessel travelled and where it reported to have fished). While finding the obvious anomalies between such datasets may be a simple task, discovering more subtle inconsistencies can be challenging when the datasets describe many events that cover large geographic and temporal ranges. This paper presents a geovisual analytics approach to this problem domain, automatically extracting potential event anomalies from the data, visualizing these on a map, and providing interactive filtering tools to allow expert analysts to discover and analyze patterns that are of interest. A case study is presented, illustrating the value of the approach for discovering anomalies between commercial fishing vessel movement data and their reported fishing locations. Field trial evaluations confirm the benefits of this geovisual analytics approach for supporting real-world data analyst needs.
{"title":"Supporting Event-based Geospatial Anomaly Detection with Geovisual Analytics","authors":"O. Hoeber, M. Hasan","doi":"10.5220/0005268000170028","DOIUrl":"https://doi.org/10.5220/0005268000170028","url":null,"abstract":"Collecting multiple geospatial datasets that describe the same real-world events can be useful in monitoring and enforcement situations (e.g., independently tracking where a fishing vessel travelled and where it reported to have fished). While finding the obvious anomalies between such datasets may be a simple task, discovering more subtle inconsistencies can be challenging when the datasets describe many events that cover large geographic and temporal ranges. This paper presents a geovisual analytics approach to this problem domain, automatically extracting potential event anomalies from the data, visualizing these on a map, and providing interactive filtering tools to allow expert analysts to discover and analyze patterns that are of interest. A case study is presented, illustrating the value of the approach for discovering anomalies between commercial fishing vessel movement data and their reported fishing locations. Field trial evaluations confirm the benefits of this geovisual analytics approach for supporting real-world data analyst needs.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"118 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134476531","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 : 1900-01-01DOI: 10.5220/0005360002680273
F. Windhager, Albert Amor-Amoros, M. Smuc, P. Federico, L. Zenk, Silvia Miksch
Patents, archived as large collections of semi-structured text documents, contain valuable information about historical trends and current states of R&D fields, as well as performances of single inventors and companies. Specific methods are needed to unlock this information and enable its insightful analysis by investors, executives, funding agencies, and policy makers. In this position paper, we propose an approach based on modelling patent repositories as multivariate temporal networks, and examining them by the means of specific visual analytics methods. We illustrate the potential of our approach by discussing two use-cases: the determination of emerging research fields in general and within companies, as well as the identification of inventors characterized by different temporal paths of productivity.
{"title":"A Concept for the Exploratory Visualization of Patent Network Dynamics","authors":"F. Windhager, Albert Amor-Amoros, M. Smuc, P. Federico, L. Zenk, Silvia Miksch","doi":"10.5220/0005360002680273","DOIUrl":"https://doi.org/10.5220/0005360002680273","url":null,"abstract":"Patents, archived as large collections of semi-structured text documents, contain valuable information about historical trends and current states of R&D fields, as well as performances of single inventors and companies. Specific methods are needed to unlock this information and enable its insightful analysis by investors, executives, funding agencies, and policy makers. In this position paper, we propose an approach based on modelling patent repositories as multivariate temporal networks, and examining them by the means of specific visual analytics methods. We illustrate the potential of our approach by discussing two use-cases: the determination of emerging research fields in general and within companies, as well as the identification of inventors characterized by different temporal paths of productivity.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121352723","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 : 1900-01-01DOI: 10.5220/0005305002330238
Michel F. Randrianandrasana, Shahzad Mumtaz, I. Nabney
Heterogeneous and incomplete datasets are common in many real-world visualisation applications. The probabilistic nature of the Generative Topographic Mapping (GTM), which was originally developed for complete continuous data, can be extended to model heterogeneous (i.e. containing both continuous and discrete values) and missing data. This paper describes and assesses the resulting model on both synthetic and real-world heterogeneous data with missing values.
{"title":"Visualisation of Heterogeneous Data with the Generalised Generative Topographic Mapping","authors":"Michel F. Randrianandrasana, Shahzad Mumtaz, I. Nabney","doi":"10.5220/0005305002330238","DOIUrl":"https://doi.org/10.5220/0005305002330238","url":null,"abstract":"Heterogeneous and incomplete datasets are common in many real-world visualisation applications. The probabilistic nature of the Generative Topographic Mapping (GTM), which was originally developed for complete continuous data, can be extended to model heterogeneous (i.e. containing both continuous and discrete values) and missing data. This paper describes and assesses the resulting model on both synthetic and real-world heterogeneous data with missing values.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116936314","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 : 1900-01-01DOI: 10.5220/0005266601090116
G. D. Lozzo, M. D. Bartolomeo, M. Patrignani, G. Battista, D. Cannone, Sergio Tortora
We consider the task of visually exploring relationships (such as established connections, similarity, reachability, etc) among a set of georeferenced entities, i.e., entities that have geographic data associated with them. A novel 2.5D paradigm is proposed that provides a robust and practical solution based on separating and then integrating back again the networked and geographical dimensions of the input dataset. This allows us to easily cope with partial or incomplete geographic annotations, to reduce cluttering of close entities, and to address focus-plus-context visualization issues. Typical application domains include, for example, coordinating search and rescue teams or medical evacuation squads, monitoring ad-hoc networks, exploring location-based social networks and, more in general, visualizing relational datasets including geographic annotations.
{"title":"Drawing Georeferenced Graphs - Combining Graph Drawing and Geographic Data","authors":"G. D. Lozzo, M. D. Bartolomeo, M. Patrignani, G. Battista, D. Cannone, Sergio Tortora","doi":"10.5220/0005266601090116","DOIUrl":"https://doi.org/10.5220/0005266601090116","url":null,"abstract":"We consider the task of visually exploring relationships (such as established connections, similarity, reachability, etc) among a set of georeferenced entities, i.e., entities that have geographic data associated with them. A novel 2.5D paradigm is proposed that provides a robust and practical solution based on separating and then integrating back again the networked and geographical dimensions of the input dataset. This allows us to easily cope with partial or incomplete geographic annotations, to reduce cluttering of close entities, and to address focus-plus-context visualization issues. Typical application domains include, for example, coordinating search and rescue teams or medical evacuation squads, monitoring ad-hoc networks, exploring location-based social networks and, more in general, visualizing relational datasets including geographic annotations.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127761581","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 : 1900-01-01DOI: 10.5220/0005342701710176
Richard Müller, Dirk Zeckzer
In this paper, we present the recursive disk metaphor, a glyph-based visualization for software visualization. The metaphor represents all important structural aspects and relations of software using nested circular glyphs. The result is a shape with an inner structural consistency and a completely defined orientation. We compare the recursive disk metaphor to other state-of-the-art 2D approaches that visualize structural aspects and relations of software. Further, a case study shows the feasibility and scalability of the approach by visualizing an open source software system in a browser.
{"title":"The Recursive Disk Metaphor - A Glyph-based Approach for Software Visualization","authors":"Richard Müller, Dirk Zeckzer","doi":"10.5220/0005342701710176","DOIUrl":"https://doi.org/10.5220/0005342701710176","url":null,"abstract":"In this paper, we present the recursive disk metaphor, a glyph-based visualization for software visualization. The metaphor represents all important structural aspects and relations of software using nested circular glyphs. The result is a shape with an inner structural consistency and a completely defined orientation. We compare the recursive disk metaphor to other state-of-the-art 2D approaches that visualize structural aspects and relations of software. Further, a case study shows the feasibility and scalability of the approach by visualizing an open source software system in a browser.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122207310","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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