Tulip 5.
Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on more than 16 years of research and development, Tulip is built on a suite of tools and techniques, that can be used to address a large variety of domain-specific problems. With Tulip, we aim to provide Python and/or C++ developers a complete library, supporting the design of interactive information visualization applications for relational data, that can be customized to address a wide range of visualization problems. In its current iteration, Tulip enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. This development pipeline makes the framework efficient for creating research prototypes as well as developing end-user applications. The recent addition of a complete Python programming layer wraps up Tulip as an ideal tool for fast prototyping and treatment automation, allowing to focus on problem solving, and as a great system for teaching purposes at all education levels.
David Auber, Daniel Archambault, Romain Bourqui, Maylis Delest, Jonathan Dubois, Antoine Lambert, Patrick Mary, Morgan Mathiaut, Guy Melançon, Bruno Pinaud, Benjamin Renoust, Jason Vallet
In Encyclopedia of Social Network Analysis and Mining, Springer, 2017. .pdf .bib
TULIP 4.
Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on more than 15 years of research and development, Tulip is built on a suite of tools and techniques , that can be used to address a large variety of domain-specific problems. With Tulip, we aim to provide Python and/or C++ developers a complete library, supporting the design of interactive information visualization applications for relational data, that can be customized to address a wide range of visualization problems. In its current iteration, Tulip enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. This development pipeline makes the framework efficient for creating research prototypes as well as developing end-user applications. The recent addition of a complete Python programming layer wraps up Tulip as an ideal tool for fast prototyping and treatment automation, allowing to focus on problem solving, and as a great system for teaching purposes at all education levels.
David Auber, Romain Bourqui, Maylis Delest, Antoine Lambert, Patrick Mary, Guy Melançon, Bruno Pinaud, Benjamin Renoust, Jason Vallet.
In [Research Report] LaBRI - Laboratoire Bordelais de Recherche en Informatique. 2016. .pdf .bib
Tulip III.
The Graph Visualization Framework Tulip now enjoys 10 years of user experience, and has matured its architecture and development cycle. Originally designed to interactively navigate large graphs, the framework integrates state-of-the-art software engineering concepts and good practices. It offers a large panel of graphical representations (traditional graph drawing as well as alternate representations). Tulip is most useful in a data mining and knowledge discovery context, allowing users to easily add their own data analysis and computing routines through its plug-in architecture.
David Auber, Daniel Archambault, Romain Bourqui, Maylis Delest, Jonathan Dubois, Bruno Pinaud, Antoine Lambert, Patrick Mary, Morgan Mathiaut, Guy Melançon
In Encyclopedia of Social Network Analysis and Mining, 2014. .pdf .bib
The Tulip 3 Framework: A Scalable Software Library for Information Visualization Applications Based on Relational Data.
Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on a decade of research and development of this framework, we present the architecture, consisting of a suite of tools and techniques, that can be used to address a large variety of domain-specific problems. With Tulip, we aim to provide the developer with a complete library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems he or she is addressing. The current framework enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. The software model facilitates the reuse of components and allows the developers to focus on programming their application. This development pipeline makes the framework efficient for research prototyping as well as the development of end-user applications.
David Auber, Daniel Archambault, Romain Bourqui, Antoine Lambert, Morgan Mathiaut, Patrick Mary, Maylis Delest, Jonathan Dubois, Guy Melançon.
In [Research Report] RR-7860, INRIA. 2012, pp.31. .pdf .bib
Tulip: a Scalable Graph Visualization Framework.
The Graph Visualization Framework Tulip now enjoys 10 years of user experience, and has matured its architecture and development cycle. Originally designed to interactively navigate large graphs, the framework integrates state-of-the-art software engineering concepts and good practices. It offers a large panel of graphical representations (traditional graph drawing as well as alternate representations). Tulip is most useful in a data mining and knowledge discovery context, allowing users to easily add their own data analysis and computing routines through its plug-in architecture.
David Auber, Patrick Mary, Morgan Mathiaut, Jonathan Dubois, Antoine Lambert, Daniel Archambault, Romain Bourqui, Bruno Pinaud, Maylis Delest, Guy Melançon
In Extraction et Gestion des Connaissances (EGC) 2010, Hammamet, Tunisia. RNTI, RNTI E-19, pp.623-624, 2010, Extraction et Gestion des connaissances EGC'2010. .bib
Visualisation de graphes avec Tulip : exploration interactive de grandes masses de données en appui à la fouille de données et à l'extraction de connaissances.
Cet article décrit une étude de cas exhibant les qualités de la plateforme de visualisation de graphes Tulip, démontrant l'apport de la visualisation à la fouille de données interactive et à l'extraction de connaissances. Le calcul d'un graphe à partir d'indices de similarité est un exemple typique où l'exploration visuelle et interactive de graphes vient en appui au travail de fouille de données. Nous penchons sur le cas où l'on souhaite étudier une collection de documents afin d'avoir une idée des thématiques abordées dans la collection.
David Auber, Yves Chiricota, Maylis Delest, Guy Melançon, Jean-Philippe Domenger, Patrick Mary
In EGC'07: Extraction et Gestion de Connaissances, Namur, Belgique, France. Cépaduès, pp.147-156, 2007. .pdf .bib
Tulip : A huge graph visualisation framework.
The research by the information visualization community (“Info Viz”) shows clearly that using a visual representation of data-sets enables faster analysis by the end users. Several scientific reasons explain these results. First of all, the visual perception system is the most powerful of all the human perception systems. In the human brain, 70% of the receptors and 40% of the cortex are used for the vision process. Furthermore, human beings are better at “recognition” tasks than at “memorization” tasks. This implies that textual representations are less efficient than visual metaphors when one wants to analyze huge data-sets. This comes from the fact that reading is both a memorization task and a recognition task.
David Auber
In Graph Drawing Software, Springer Berlin Heidelberg, pp.105-126, 2004, Mathematics and Visualization. .bib
