Pub Date : 2015-03-14DOI: 10.5220/0005259900050016
V. Kurlin
We introduce simple codes and fast visualization tools for knotted structures in molecules and neural networks. Knots, links and more general knotted graphs are studied up to an ambient isotopy in Euclidean 3-space. A knotted graph can be represented by a plane diagram or by an abstract Gauss code. First we recognize in linear time if an abstract Gauss code represents an actual graph embedded in 3-space. Second we design a fast algorithm for drawing any knotted graph in the 3-page book, which is a union of 3 half-planes along their common boundary line. The running time of our drawing algorithm is linear in the length of a Gauss code of a given graph. Three-page embeddings provide simple linear codes of knotted graphs so that the isotopy problem for all graphs in 3-space completely reduces to a word problem in finitely presented semigroups.
{"title":"A Linear Time Algorithm for Visualizing Knotted Structures in 3 Pages","authors":"V. Kurlin","doi":"10.5220/0005259900050016","DOIUrl":"https://doi.org/10.5220/0005259900050016","url":null,"abstract":"We introduce simple codes and fast visualization tools for knotted structures in molecules and neural networks. Knots, links and more general knotted graphs are studied up to an ambient isotopy in Euclidean 3-space. A knotted graph can be represented by a plane diagram or by an abstract Gauss code. First we recognize in linear time if an abstract Gauss code represents an actual graph embedded in 3-space. Second we design a fast algorithm for drawing any knotted graph in the 3-page book, which is a union of 3 half-planes along their common boundary line. The running time of our drawing algorithm is linear in the length of a Gauss code of a given graph. Three-page embeddings provide simple linear codes of knotted graphs so that the isotopy problem for all graphs in 3-space completely reduces to a word problem in finitely presented semigroups.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124653010","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 : 2015-03-11DOI: 10.5220/0005265301010108
P. Carvalho, Patrik Hitzelberger, B. Otjacques, F. Bouali, G. Venturini
New and different information sources have appeared over the past years (e.g. Blogs, Media, Open Data, Scientific Data and Social Networks). The variety of these sources is growing and the related data volume does not cease to increase exponentially. Open Data (OD) initiatives and platforms are one of the current major data producers, also because the topic seems to be important for many governments world-wide. Given the many fields and sectors involved, OD brings high business and societal potential. The amount and diversity of available information is high. However, analysing and understanding OD in order to exploit is far from being an easy task. Several problems and constraints must be solved. Information Visualization (InfoVis) can help to give a graphical idea of the processed files structure. Given that OD is provided very often as tabular data, this paper focuses on OD CSV files. It presents an overview on the analysis of tabular information. Finally, the paper describes the role of Information Visualization and the way it may help the end-user to understand quickly the structure and issues of OD CSV files.
{"title":"Information Visualization for CSV Open Data Files Structure Analysis","authors":"P. Carvalho, Patrik Hitzelberger, B. Otjacques, F. Bouali, G. Venturini","doi":"10.5220/0005265301010108","DOIUrl":"https://doi.org/10.5220/0005265301010108","url":null,"abstract":"New and different information sources have appeared over the past years (e.g. Blogs, Media, Open Data, Scientific Data and Social Networks). The variety of these sources is growing and the related data volume does not cease to increase exponentially. Open Data (OD) initiatives and platforms are one of the current major data producers, also because the topic seems to be important for many governments world-wide. Given the many fields and sectors involved, OD brings high business and societal potential. The amount and diversity of available information is high. However, analysing and understanding OD in order to exploit is far from being an easy task. Several problems and constraints must be solved. Information Visualization (InfoVis) can help to give a graphical idea of the processed files structure. Given that OD is provided very often as tabular data, this paper focuses on OD CSV files. It presents an overview on the analysis of tabular information. Finally, the paper describes the role of Information Visualization and the way it may help the end-user to understand quickly the structure and issues of OD CSV files.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123371133","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 : 2011-03-05DOI: 10.5220/0003354202560261
Hendrik Rohn, Christian Klukas, Falk Schreiber, Falk Schreiber
{"title":"VISUAL ANALYTICS OF MULTIMODAL BIOLOGICAL DATA","authors":"Hendrik Rohn, Christian Klukas, Falk Schreiber, Falk Schreiber","doi":"10.5220/0003354202560261","DOIUrl":"https://doi.org/10.5220/0003354202560261","url":null,"abstract":"","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126611867","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: 10.5220/0005300901170122
B. Thooris, Daniel Pomarède
In the context of our project COAST (for Computational Astrophysics), a program of massively parallel numerical simulations in astrophysics involving astrophysicists and software engineers, we have developed visualization tools to analyse the massive amount of data produced in these simulations. We present in this paper the SDvision code capabilities with examples of visualization of cosmology and astrophysical simulations realized with hydrodynamics codes, and more results in other domains of physics, like plasma or particles physics. Recently, the SDvision 3D visualization software has been improved to cope with the analysis of astronomical surveys catalogues, databases of multiple data products including redshifts, peculiar velocities, reconstructed density and velocity fields. On the basis of the various visualization techniques offered by the SDvision software, that rely on multicore computing and OpenGL hardware acceleration, we have created maps displaying the structure of the Local Universe where the most prominent features such as voids, clusters of galaxies, filaments and walls, are identified and named.
{"title":"Visualization of Large Scientific Datasets - Analysis of Numerical Simulation Data and Astronomical Surveys Catalogues","authors":"B. Thooris, Daniel Pomarède","doi":"10.5220/0005300901170122","DOIUrl":"https://doi.org/10.5220/0005300901170122","url":null,"abstract":"In the context of our project COAST (for Computational Astrophysics), a program of massively parallel numerical simulations in astrophysics involving astrophysicists and software engineers, we have developed visualization tools to analyse the massive amount of data produced in these simulations. We present in this paper the SDvision code capabilities with examples of visualization of cosmology and astrophysical simulations realized with hydrodynamics codes, and more results in other domains of physics, like plasma or particles physics. Recently, the SDvision 3D visualization software has been improved to cope with the analysis of astronomical surveys catalogues, databases of multiple data products including redshifts, peculiar velocities, reconstructed density and velocity fields. On the basis of the various visualization techniques offered by the SDvision software, that rely on multicore computing and OpenGL hardware acceleration, we have created maps displaying the structure of the Local Universe where the most prominent features such as voids, clusters of galaxies, filaments and walls, are identified and named.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"8 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":"127112010","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: 10.5220/0005356501820189
M. Nunes, K. Matkovič, K. Bühler
Interactive Visual Analysis has been widely used for the reason that it allows users to investigate highly complex data in coordinated multiple views, showing different perspectives over data. In order to relate data, multiple techniques of brushing have been introduced. This work extends the state of the art by introducing the Convex Hull (CH) Brush, which is a new way of selecting and interpreting high dimensional data in scatter plot (SP) views. By using a combination of brushes through linked views, the CH-Brush allows the selection and clustering of values that are not typically defined by SP ranges, in spite of sharing similarities. In CHBrushing is also able to visually report the existence of correlation between variables. Furthermore, we discuss CH-Brushing sensitivity and the application of smoothness. We use synthetic data to support our rationale and clarify the intrinsic meanings of CH-Brushing in scatter plots. We also report on the first experience on using the CH-Brush in a real-world medical case.
{"title":"Convex Hull Brushing in Scatter Plots - Multi-dimensional Correlation Analysis","authors":"M. Nunes, K. Matkovič, K. Bühler","doi":"10.5220/0005356501820189","DOIUrl":"https://doi.org/10.5220/0005356501820189","url":null,"abstract":"Interactive Visual Analysis has been widely used for the reason that it allows users to investigate highly complex data in coordinated multiple views, showing different perspectives over data. In order to relate data, multiple techniques of brushing have been introduced. This work extends the state of the art by introducing the Convex Hull (CH) Brush, which is a new way of selecting and interpreting high dimensional data in scatter plot (SP) views. By using a combination of brushes through linked views, the CH-Brush allows the selection and clustering of values that are not typically defined by SP ranges, in spite of sharing similarities. In CHBrushing is also able to visually report the existence of correlation between variables. Furthermore, we discuss CH-Brushing sensitivity and the application of smoothness. We use synthetic data to support our rationale and clarify the intrinsic meanings of CH-Brushing in scatter plots. We also report on the first experience on using the CH-Brush in a real-world medical case.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"16 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":"124810207","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: 10.5220/0005314801630170
V. Lombardo, Antonio Pizzo
Drama, the art that displays characters performing live actions in telling a story, is pervasive in cultures and media. The study of drama often resorts to hierarchical structures to explain the sequences of incidents that occur. Hierarchies provide an explanation of why incidents are in the sequence or cluster elements into subsequences that form a meaningful structure. This paper addresses the visualization of drama hierarchies. The paper inspects the peculiar features of drama hierarchies and proposes a visualization built upon the metaphors of tree mapping and timeline, respectively. The visualizations are preliminarily applied in tasks of analysis and interpretation in supporting teaching and research of drama scholars.
{"title":"The Visualization of Drama Hierarchies","authors":"V. Lombardo, Antonio Pizzo","doi":"10.5220/0005314801630170","DOIUrl":"https://doi.org/10.5220/0005314801630170","url":null,"abstract":"Drama, the art that displays characters performing live actions in telling a story, is pervasive in cultures and media. The study of drama often resorts to hierarchical structures to explain the sequences of incidents that occur. Hierarchies provide an explanation of why incidents are in the sequence or cluster elements into subsequences that form a meaningful structure. This paper addresses the visualization of drama hierarchies. The paper inspects the peculiar features of drama hierarchies and proposes a visualization built upon the metaphors of tree mapping and timeline, respectively. The visualizations are preliminarily applied in tasks of analysis and interpretation in supporting teaching and research of drama scholars.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"16 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":"126030237","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: 10.5220/0005311001570162
F. Poiesi, A. Cavallaro
We present an interactive visualizer that enables the exploration, measurement, analysis and manipulation of trajectories. Trajectories can be generated either automatically by multi-target tracking algorithms or manually by human annotators. The visualizer helps understanding the behavior of targets, correcting tracking results and quantifying the performance of tracking algorithms. The input video can be overlaid to compare ideal and estimated target locations. The code of the visualizer (C++ with openFrameworks) is open source.
{"title":"MTTV - An Interactive Trajectory Visualization and Analysis Tool","authors":"F. Poiesi, A. Cavallaro","doi":"10.5220/0005311001570162","DOIUrl":"https://doi.org/10.5220/0005311001570162","url":null,"abstract":"We present an interactive visualizer that enables the exploration, measurement, analysis and manipulation of trajectories. Trajectories can be generated either automatically by multi-target tracking algorithms or manually by human annotators. The visualizer helps understanding the behavior of targets, correcting tracking results and quantifying the performance of tracking algorithms. The input video can be overlaid to compare ideal and estimated target locations. The code of the visualizer (C++ with openFrameworks) is open source.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"38 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":"124098585","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: 10.5220/0005305901310138
M. D. Ridder, Karsten Klein, Jinman Kim
We present CereVA, a web-based interface for the visual analysis of brain activity data. CereVA combines 2D and 3D visualizations and allows the user to interactively explore and compare brain activity data sets. The web-based interface combines several linked graphical representations of the network data, allowing for tight integration of different visualizations. The data is presented in the anatomical context within a 3D volume rendering, by node-link visualizations of connectivity networks, and by a matrix view of the data. In addition, our approach provides graph-theoretical analysis of the connectivity networks. Our solution supports several analysis tasks, including the comparison of connectivity networks, the analysis of correlation patterns, and the aggregation of networks, e.g. over a population.
{"title":"CereVA - Visual Analysis of Functional Brain Connectivity","authors":"M. D. Ridder, Karsten Klein, Jinman Kim","doi":"10.5220/0005305901310138","DOIUrl":"https://doi.org/10.5220/0005305901310138","url":null,"abstract":"We present CereVA, a web-based interface for the visual analysis of brain activity data. CereVA combines 2D and 3D visualizations and allows the user to interactively explore and compare brain activity data sets. The web-based interface combines several linked graphical representations of the network data, allowing for tight integration of different visualizations. The data is presented in the anatomical context within a 3D volume rendering, by node-link visualizations of connectivity networks, and by a matrix view of the data. In addition, our approach provides graph-theoretical analysis of the connectivity networks. Our solution supports several analysis tasks, including the comparison of connectivity networks, the analysis of correlation patterns, and the aggregation of networks, e.g. over a population.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"391 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":"114811754","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: 10.5220/0005297102190224
H. C. G. Leitão, R. Saracchini, J. Stolfi
This article describes a three-channel encoding of nucleotide sequences, and proper formulas for filtering and downsampling such encoded sequences for multi-scale signal analysis. With proper interpolation, the encoded sequences can be visualized as curves in three-dimensional space. The filtering uses Gaussian-like smoothing kernels, chosen so that all levels of the multi-scale pyramid (except the original curve) are practically free from aliasing artifacts and have the same degree of smoothing. With these precautions, the overall shape of the space curve is robust under small changes in the DNA sequence, such as single-point mutations, insertions, deletions, and shifts.
{"title":"Geometric Encoding, Filtering, and Visualization of Genomic Sequences","authors":"H. C. G. Leitão, R. Saracchini, J. Stolfi","doi":"10.5220/0005297102190224","DOIUrl":"https://doi.org/10.5220/0005297102190224","url":null,"abstract":"This article describes a three-channel encoding of nucleotide sequences, and proper formulas for filtering and downsampling such encoded sequences for multi-scale signal analysis. With proper interpolation, the encoded sequences can be visualized as curves in three-dimensional space. The filtering uses Gaussian-like smoothing kernels, chosen so that all levels of the multi-scale pyramid (except the original curve) are practically free from aliasing artifacts and have the same degree of smoothing. With these precautions, the overall shape of the space curve is robust under small changes in the DNA sequence, such as single-point mutations, insertions, deletions, and shifts.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"56 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":"114859528","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: 10.5220/0005235500850092
Paul Klemm, S. Glaßer, K. Lawonn, Marko Rak, H. Völzke, K. Hegenscheid, B. Preim
Epidemiology aims to provide insight into disease causations. Hence, subject groups (cohorts) are analyzed to correlate the subjects’ varying lifestyles, their medical properties and diseases. Recently, these cohort studies comprise medical image data. We assess potential relations between image-derived variables of the lumbar spine with lower back pain in a cross-sectional study. Therefore, an Interactive Visual Analysis (IVA) framework was created and tested with 2,540 segmented lumbar spine data sets. The segmentation results are evaluated and quantified by employing shape-describing variables, such as spine canal curvature and torsion. We analyze mutual dependencies among shape-describing variables and non-image variables, e.g., pain indicators. Therefore, we automatically train a decision tree classifier for each non-image variable. We provide an IVA technique to compare classifiers with a decision tree quality plot. As a first result, we conclude that image-based variables are only sufficient to describe lifestyle factors within the data. A correlation between lumbar spine shape and lower back pain could not be found with the automatically trained classifiers. However, the presented approach is a valuable extension for the IVA of epidemiological data. Hence, relations between non-image variables were successfully detected and described.
{"title":"Interactive Visual Analysis of Lumbar Back Pain - What the Lumbar Spine Tells About Your Life","authors":"Paul Klemm, S. Glaßer, K. Lawonn, Marko Rak, H. Völzke, K. Hegenscheid, B. Preim","doi":"10.5220/0005235500850092","DOIUrl":"https://doi.org/10.5220/0005235500850092","url":null,"abstract":"Epidemiology aims to provide insight into disease causations. Hence, subject groups (cohorts) are analyzed to correlate the subjects’ varying lifestyles, their medical properties and diseases. Recently, these cohort studies comprise medical image data. We assess potential relations between image-derived variables of the lumbar spine with lower back pain in a cross-sectional study. Therefore, an Interactive Visual Analysis (IVA) framework was created and tested with 2,540 segmented lumbar spine data sets. The segmentation results are evaluated and quantified by employing shape-describing variables, such as spine canal curvature and torsion. We analyze mutual dependencies among shape-describing variables and non-image variables, e.g., pain indicators. Therefore, we automatically train a decision tree classifier for each non-image variable. We provide an IVA technique to compare classifiers with a decision tree quality plot. As a first result, we conclude that image-based variables are only sufficient to describe lifestyle factors within the data. A correlation between lumbar spine shape and lower back pain could not be found with the automatically trained classifiers. However, the presented approach is a valuable extension for the IVA of epidemiological data. Hence, relations between non-image variables were successfully detected and described.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"27 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":"131580313","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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