Computing Storyline Visualizations with Few Block Crossings

Dijk, Thomas C. van; Lipp, Fabian; Markfelder, Peter; Wolff, Alexander

doi:10.1007/978-3-319-73915-1_29

Cited by 12 publications

(20 citation statements)

References 14 publications

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“…Curiosamente, es la tira cómica y no el mapa de Minard el que ha derivado en la aparición de una técnica de visualización de datos denominada storyline (Ogawa y Ma, 2010); los autores adaptaron la representación propuesta por Munroe al análisis de las interacciones entre desarrolladores de un proyecto software. Desde entonces se han sucedido diversas propuestas para la automatización de la creación de este tipo de diagramas (T. Chen et al, 2012;Tanahashi y Ma, 2012;van Dijk et al, 2017;Tang et al, 2018;Padia et al, 2018;Tang et al, 2020;Araya et al, 2021).…”

Section: Las Dos Culturas De La Visualización De Datos En La Historiaunclassified

Visualización de datos

Therón

2021

FJC

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El análisis de datos ha recurrido a la representación visual para encontrar u ofrecer explicaciones desde sus orígenes. Las capacidades de almacenamiento y procesamiento no han parado de crecer gracias al avance de la computación. Paralelamente, gracias a una democratización tecnológica, las contribuciones artísticas en la visualización de datos se han multiplicado, aportando nuevas formas creativas y planteamientos diferentes a los propios de la ciencia. Este artículo analiza las influencias recíprocas entre las artes y las ciencias en torno a las visualizaciones de datos. Se traza una visión panorámica de la Visualización de Datos mediante un análisis de la dedicación en las diversas disciplinas a este tema de estudio recogido en las principales bases de datos bibliográficas, así como de la creciente comunidad de práctica que acoge en la actualidad a científicos, diseñadores, artistas y otros perfiles profesionales. A través de una serie de ejemplos históricos que van desde la prehistoria hasta la actualidad, se ponen de manifiesto diversas líneas de influencia en las que arte y ciencia han propiciado avances en la comunicación de fenómenos o el planteamiento de preguntas a partir de los datos. Finalmente, se ofrecen unas reflexiones sobre los retos que afronta la Visualización de Datos y las oportunidades que de ellos se derivan.

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Section: Las Dos Culturas De La Visualización De Datos En La Historiaunclassified

Visualización de datos

Therón

2021

FJC

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“…Branch Permutation. We exploit a state-of-the-art algorithm for classical storyline visualizations, namely the algorithm by van Dijk et al [4] based on a SAT formulation, which optimally solves the problem of minimizing block crossings. To this aim, we transform the output of Step 1 into an instance for a classical storyline visualization: Each actor tree is partitioned into a set of edge-disjoint paths by duplicating each node with k ≥ 2 children into k nodes each having one child (see Fig.…”

Section: The Sua Algorithmic Pipelinementioning

confidence: 99%

“…3(a)). Each path is processed by the algorithm in [4] as a distinct actor. All copies of the same node are then recombined into a single node to restore the tree.…”

Section: The Sua Algorithmic Pipelinementioning

confidence: 99%

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Storyline Visualizations with Ubiquitous Actors

Giacomo¹,

Didimo²,

Liotta³

et al. 2020

Preprint

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Storyline visualizations depict the temporal dynamics of social interactions, as they describe how groups of actors (individuals or organizations) change over time. A common constraint in storyline visualizations is that an actor cannot belong to two different groups at the same time instant. However, this constraint may be too severe in some application scenarios, thus we generalize the model by allowing an actor to simultaneously belong to distinct groups at any point in time. We call this model Storyline with Ubiquitous Actors (SUA). Essential to our model is that an actor is represented as a tree rather than a single line. We describe an algorithmic pipeline to compute storyline visualizations in the SUA model and discuss case studies on publication data.

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“…[18,21]) and storyline visualizations (see e.g. [22]). However, most approaches from the graph drawing literature, including orthogonal layout methods, produce outputs that have a distinct style and are not suited to be customized to adhere to engineering drawing conventions.…”

Section: Introductionmentioning

confidence: 99%

Automated Drawing of Railway Schematics Using Numerical Optimization in SAT

Luteberget¹,

Claessen

Johansen

2019

Lecture Notes in Computer Science

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Schematic drawings showing railway tracks and equipment are commonly used to visualize railway operations and to communicate system specifications and construction blueprints. Recent advances in on-line collaboration and modeling tools have raised the expectations for quickly making changes to models, resulting in frequent changes to layouts, text, and/or symbols in schematic drawings. Automating the creation of high-quality schematic views from geographical and topological models can help engineers produce and update drawings efficiently. This paper describes three methods for automatically producing schematic railway drawings with increasing level of quality and control over the result. The final method, implemented in the tool that we present, can use any combination of the following optimization criteria, which have different priorities in different use cases: width and height of the drawing, the diagonal line lengths, and the number of bends. We show how to encode schematic railway drawings as an optimization problem over Boolean and numerical domains, using combinations of unary number encoding, lazy difference constraints, and numerical optimization into an incremental SAT formulation. We compare resulting drawings from each of the three approaches, applied to models of real-world engineering projects and existing infrastructure. We also show how to add symbols and labels to the track plan, which is important for the usefulness of the final outputs. Since the proposed tool is customizable and efficiently produces high-quality drawings from railML 2.x models, it can be used (as it is or extended) both as an integrated module in an industrial design tool like RailCOMPLETE, or by researchers for visualization purposes.

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Computing Storyline Visualizations with Few Block Crossings

Cited by 12 publications

References 14 publications

Visualización de datos

Visualización de datos

Storyline Visualizations with Ubiquitous Actors

Automated Drawing of Railway Schematics Using Numerical Optimization in SAT

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