yt:channel:UCc4xp5KL__Tkotz1VGuLPiQ UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2009-06-03T10:01:01+00:00 yt:video:Okdfmxzab-M Okdfmxzab-M UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2017-08-01T15:46:15+00:00 2017-08-01T20:40:38+00:00 Taggle: Scaling Table Visualization through Aggregation (Poster @ InfoVis ’17) # Authors Katarina Furmanova, Miroslava Jaresova, Bikram Kawan, Holger Stitz, Martin Ennemoser, Samuel Gratzl, Alexander Lex, Marc Streit # Abstract Tabular data visualizations are easy to understand and powerful at communicating patterns in datasets, especially when paired with interactions such as sorting. In this work we present Taggle, a novel visualization technique for large and complex tables. We consider datasets that are composed of columns of categorical, or numerical data, in addition to homogeneous matrices. The key contribution of Taggle is its ability to aggregate data subsets (rows and columns) on demand based on user-defined grouping rules. Different visual representations for individual cells and aggregated subsets are available. The aggregation strategy is complemented by the ability to sort hierarchically and by rich data selection and filtering capabilities. We demonstrate the usefulness of Taggle using an AIDS dataset for 160 countries. yt:video:z8SSqQfrbG8 z8SSqQfrbG8 UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2017-08-01T15:26:07+00:00 2017-08-01T15:46:38+00:00 Provenance-Based Visualization Retrieval (Poster @ VAST ’17) # Authors Holger Stitz, Samuel Gratzl, Harald Piringer, Marc Streit # Abstract Storing interaction provenance generates a knowledge base with a large potential for recalling previous results and guiding the user in future analyses. However, search and retrieval of analysis states can become tedious without extensive creation of meta-information by the user. In this work we present an approach for an efficient retrieval of analysis states which are structured as provenance graphs of automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of the requested analysis state. Depending on the use case, this definition may be provided explicitly by the user or inferred from a reference state. We explain the definition by means of a Gapminder-inspired prototype and discuss our implementation for an effective retrieval of previous states. yt:video:XpkauEySBHw XpkauEySBHw UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2017-08-01T14:26:48+00:00 2017-08-01T15:58:39+00:00 TACO: Visual Comparison of Tables over Time (Preview Video without Audio) Homepage: http://taco.caleydo.org/ Demo: https://taco.caleydoapp.org/ # Authors Christina Niederer, Holger Stitz, Reem Hourieh, Florian Grassinger, Wolfgang Aigner, Marc Streit # Abstract Multivariate, tabular data is one of the most common data structures used in many different domains. Over time, tables can undergo changes in both structure and content, which results in multiple versions of the same table. A challenging task when working with such derived tables is to understand what exactly has changed between versions in terms of additions/deletions, reorder, merge/split, and content changes. For textual data, a variety of commonplace "diff" tools exist that support the task of investigating changes between revisions of a text. Although there are some comparison tools which assist users in inspecting differences between multiple table instances, the resulting visualizations are often difficult to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TACO, an interactive comparison tool that visualizes effectively the differences between multiple tables at various levels of detail. With TACO we show (1) the aggregated differences between multiple table versions over time, (2) the aggregated changes between two selected table versions, and (3) detailed changes between the selection. To demonstrate the effectiveness of our approach, we show its application by means of two usage scenarios. yt:video:sA5oGo7GrYc sA5oGo7GrYc UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2017-08-01T14:25:23+00:00 2017-08-01T20:39:53+00:00 TACO: Visual Comparison of Tables over Time (Demonstration Video) Homepage: http://taco.caleydo.org/ Demo: https://taco.caleydoapp.org/ # Authors Christina Niederer, Holger Stitz, Reem Hourieh, Florian Grassinger, Wolfgang Aigner, Marc Streit # Abstract Multivariate, tabular data is one of the most common data structures used in many different domains. Over time, tables can undergo changes in both structure and content, which results in multiple versions of the same table. A challenging task when working with such derived tables is to understand what exactly has changed between versions in terms of additions/deletions, reorder, merge/split, and content changes. For textual data, a variety of commonplace "diff" tools exist that support the task of investigating changes between revisions of a text. Although there are some comparison tools which assist users in inspecting differences between multiple table instances, the resulting visualizations are often difficult to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TACO, an interactive comparison tool that visualizes effectively the differences between multiple tables at various levels of detail. With TACO we show (1) the aggregated differences between multiple table versions over time, (2) the aggregated changes between two selected table versions, and (3) detailed changes between the selection. To demonstrate the effectiveness of our approach, we show its application by means of two usage scenarios. yt:video:35oa5IvgtS0 35oa5IvgtS0 UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2017-04-19T00:06:13+00:00 2017-04-19T08:02:52+00:00 Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs Supplementary video for the preprint by Carolina Nobre, Nils Gehlenborg, Hilary Coon, Alexander Lex Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs bioRxiv preprint, 2017. http://vdl.sci.utah.edu/publications/2017_preprint_lineage/ yt:video:gI16xhF9evY gI16xhF9evY UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2017-04-06T20:58:07+00:00 2017-06-18T00:58:15+00:00 Interactive Visual Exploration And Refinement Of Cluster Assignments Supplementary video to the paper: Michael Kern, Alexander Lex, Nils Gehlenborg, Chris R. Johnson Interactive Visual Exploration And Refinement Of Cluster Assignments bioRxiv preprint, doi:10.1101/123844, 2017. See http://vdl.sci.utah.edu/publications/017_preprint_clustering/ for details. yt:video:yFWPCtjBlCc yFWPCtjBlCc UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2016-06-28T14:58:38+00:00 2017-03-09T12:50:32+00:00 TaCo: Comparative Visualization of Large Tabular Data (Poster @ EuroVis ’16) Tabular data plays a vital role in many different domains. In the course of a project, changes to the structure and content of tables can result in multiple instances of a table. A challenging task when working with such derived tables is to understand what exactly has changed from one version to another. Traditional comparison tools assist users in inspecting differences between multiple table instances, however, the resulting visualizations are often hard to interpret or do not scale to large tables with thousands of rows and columns. To address these challenges, we developed TaCo, an interactive comparison tool that effectively visualizes the differences between multiple tables at various levels of granularity: (1) the aggregated differences between all table instances, (2) the differences between one table compared to all others, and (3) the detailed differences between two instances. Authors Reem Hourieh, Holger Stitz, Nils Gehlenborg, and Marc Streit Published In Poster Compendium of the Eurographics/IEEE Symposium on Visualization (EuroVis ’16), Groningen, Netherlands, 2016 yt:video:dpH_qTyz-20 dpH_qTyz-20 UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2016-04-19T14:22:05+00:00 2016-04-22T18:57:54+00:00 ThermalPlot: KPMG Data Observatory - Large Screen Setup We adapted ThermalPlot for the KPMG Data Observatory, located at the Data Science Institute of the Imperial College London (https://imperial.ac.uk/dsi/). It visualizes the FTSE 250 stock market index. Further information about the ThermalPlot visualization technique can be found at the paper homepage (http://thermalplot.pipes-vs-dams.at). yt:video:XNZu_t2GhdA XNZu_t2GhdA UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2016-04-18T07:05:37+00:00 2017-06-18T01:00:16+00:00 AVOCADO: Visualization of Workflow-Derived Data Provenance for Reproducible Biomedical Research The associated paper is published at EuroVis 2016 (Computer Graphics Forum). More info at: http://www.caleydo.org/publications/2016_eurovis_avocado/ yt:video:r1DZGIYIFR0 r1DZGIYIFR0 UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2016-04-15T18:40:44+00:00 2017-07-26T11:15:33+00:00 From Visual Exploration to Storytelling and Back Again CLUE is a model of capturing the provenance of visual data analysis and using this providence to create compelling, interactive, and web-based stories. Here we introduce the model and showcase an implementation of CLUE. The associated paper is published at EuroVis 2016 (Computer Graphics Forum). More info at: http://www.caleydo.org/publications/2016_eurovis_clue/ yt:video:aZF7AC8aNXo aZF7AC8aNXo UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2016-04-12T16:45:21+00:00 2017-07-25T18:14:05+00:00 Pathfinder: Visual Analysis of Paths in Graphs Pathfinder is an interactive, query based graph visualization technique that focuses on paths: you can easily find and evaluate paths connecting two nodes in a large network. The associated paper is published at EuroVis 2016 (Computer Graphics Forum). Details at: http://www.caleydo.org/publications/2016_eurovis_pathfinder/ yt:video:hxRpf_BhgSU hxRpf_BhgSU UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2016-01-10T09:47:06+00:00 2016-09-12T21:06:51+00:00 ThermalPlot: Visualizing Multi-Attribute Time-Series Data Using a Thermal Metaphor Holger Stitz, Samuel Gratzl, Wolfgang Aigner and Marc Streit ThermalPlot: Visualizing Multi-Attribute Time-Series Data Using a Thermal Metaphor IEEE Transactions on Visualization and Computer Graphics, 2016. http://thermalplot.pipes-vs-dams.at yt:video:XMKicv9w8ic XMKicv9w8ic UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2015-11-27T07:44:47+00:00 2015-11-27T07:46:06+00:00 ThermalPlot: Visualizing Multi-Attribute Time-Series Data Using a Thermal Metaphor Poster presented at IEEE InfoVis 2015. yt:video:_Q4EBGkmZAg _Q4EBGkmZAg UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2015-09-29T12:01:58+00:00 2015-09-29T12:01:58+00:00 Interactive Visualization of Provenance Graphs for Reproducible Biomedical Research Poster @ IEEE InfoVis'15 Authors: Stefan Luger, Holger Stitz, Samuel Gratzl, Nils Gehlenborg, Marc Streit Abstract: A major challenge of data-driven biomedical research lies in the collection and representation of provenance information to ensure reproducibility of the gained results. The Refinery Platform is an integrated data management, analysis, and visualization system designed to support reproducible biomedical research. In order to communicate and reproduce multi-step analyses on datasets that contain data of hundreds of samples, it is crucial to be able to visualize the provenance graph at different levels of detail. Most existing approaches are based on node-link diagrams, however, they usually do not scale well to large graphs. Our proposed visualization technique dynamically reduces the complexity of subgraphs through hierarchical aggregation and application of a degree-of-interest (DOI) function to each node. Triggered by user interactions, such as selecting a subset of analyses or a path in the graph, unaffected parts of the graph are dynamically aggregated into a glyph representation. We further reduce the complexity of the provenance graph visualization by layering identical or similar sequences of parallel analysis steps into an aggregated sequence. yt:video:AmAPKIQZ-g4 AmAPKIQZ-g4 UCc4xp5KL__Tkotz1VGuLPiQ Caleydo Project https://www.youtube.com/channel/UCc4xp5KL__Tkotz1VGuLPiQ 2014-11-28T20:33:15+00:00 2015-10-21T06:06:22+00:00 Caleydo Entourage: analyzing experimental data across multiple pathways Demonstration of Caleydo's pathway analysis capabilities. Learn more at http://entourage.caleydo.org. Authors: Christian Partl, Anne Mai Wassermann, Marc Streit, Mark Borowsky, Hanspeter Pfister, Dieter Schmalstieg, and Alexander Lex