In this paper we would like to suggest a modification of data vases visualization that can help understanding complex data where each subject is described by a vector of synchronized signals taken in the same time intervals. The advantages of our new approach were verified when visualizing set of Brdicka curves and answering an interesting question raised in proteomics about the Brdicka curves proportionality.
{"title":"Visualization of Individuals Characterized by a Set of Synchronized Signals","authors":"Jiří Anýž, O. Štěpánková","doi":"10.1109/IV.2013.68","DOIUrl":"https://doi.org/10.1109/IV.2013.68","url":null,"abstract":"In this paper we would like to suggest a modification of data vases visualization that can help understanding complex data where each subject is described by a vector of synchronized signals taken in the same time intervals. The advantages of our new approach were verified when visualizing set of Brdicka curves and answering an interesting question raised in proteomics about the Brdicka curves proportionality.","PeriodicalId":354135,"journal":{"name":"2013 17th International Conference on Information Visualisation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126222959","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}
When exploring noisy or visually complex data, such as seismic data from the oil and gas industry, it is often the case that algorithms cannot completely identify features of interest. Human intuition must complete the process. Given the nature of intuition, this can be a source of differing interpretations depending on the human expert, thus we do not have a single feature but multiple views of a feature. Managing multi-user and multi-version interpretations, combined with version tracking, is challenging as these interpretations are often stored as geometric objects separately from the raw data and possibly in different local machines. In this paper we combine the storage of the raw data with the storage of the interpretations produced by the visualization of features by multiple user sessions. We present case studies that illustrate our system's ability to reproduce users' amendments to the interpretations of others and the ability to retrace the history of amendments to a visual feature.
{"title":"A Visualization Architecture for Collaborative Analytical and Data Provenance Activities","authors":"A. Al-Naser, M. Rasheed, D. Irving, J. Brooke","doi":"10.1109/IV.2013.34","DOIUrl":"https://doi.org/10.1109/IV.2013.34","url":null,"abstract":"When exploring noisy or visually complex data, such as seismic data from the oil and gas industry, it is often the case that algorithms cannot completely identify features of interest. Human intuition must complete the process. Given the nature of intuition, this can be a source of differing interpretations depending on the human expert, thus we do not have a single feature but multiple views of a feature. Managing multi-user and multi-version interpretations, combined with version tracking, is challenging as these interpretations are often stored as geometric objects separately from the raw data and possibly in different local machines. In this paper we combine the storage of the raw data with the storage of the interpretations produced by the visualization of features by multiple user sessions. We present case studies that illustrate our system's ability to reproduce users' amendments to the interpretations of others and the ability to retrace the history of amendments to a visual feature.","PeriodicalId":354135,"journal":{"name":"2013 17th International Conference on Information Visualisation","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127474786","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}
How can the emotions expressed by visitors after visiting a museum be analyzed through audio recordings and transcripts of interviews? Can an analysis based on colors help the qualitative researcher to identify patterns in the data? What can visual analysis of emotions tell the researcher? This paper presents a concept and some initial reflections on the data gathered from visitor experiences to identify patterns based on emotions, using a qualitative data analysis software - NVivo - or new types of 3D visual data analysis in immersive environments.
{"title":"Emotions, Words and Colors: A Strategy to Visualize and Analyze Patterns from Visitors' Narratives in Museums","authors":"Patrizia Schettino","doi":"10.1109/IV.2013.75","DOIUrl":"https://doi.org/10.1109/IV.2013.75","url":null,"abstract":"How can the emotions expressed by visitors after visiting a museum be analyzed through audio recordings and transcripts of interviews? Can an analysis based on colors help the qualitative researcher to identify patterns in the data? What can visual analysis of emotions tell the researcher? This paper presents a concept and some initial reflections on the data gathered from visitor experiences to identify patterns based on emotions, using a qualitative data analysis software - NVivo - or new types of 3D visual data analysis in immersive environments.","PeriodicalId":354135,"journal":{"name":"2013 17th International Conference on Information Visualisation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131281397","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}
João Miguel Santos, B. Santos, Paulo Dias, S. Silva, Carlos Ferreira
Visualizing large family structures is becoming increasingly important, as more genealogical data becomes available. A space-filling h-tree layout pedigree has been recently proposed to make better use of the available space than traditional representations. In a previous paper we applauded the technique's usage of available space but remarked that it makes generation identification difficult and does not allow navigating to descendants of represented individuals. A set of extensions was proposed to help overcome these limitations and a preliminary evaluation suggested that those extensions enhance the original technique. This paper presents a more thorough evaluation carried out to assess if and how the proposed extensions improve the original h-tree layout pedigree technique. Results suggest that these extensions improve user performance on some tasks, effectively provide new functionality, and generally enhance user experience.
{"title":"Extending the H-Tree Layout Pedigree: An Evaluation","authors":"João Miguel Santos, B. Santos, Paulo Dias, S. Silva, Carlos Ferreira","doi":"10.1109/IV.2013.56","DOIUrl":"https://doi.org/10.1109/IV.2013.56","url":null,"abstract":"Visualizing large family structures is becoming increasingly important, as more genealogical data becomes available. A space-filling h-tree layout pedigree has been recently proposed to make better use of the available space than traditional representations. In a previous paper we applauded the technique's usage of available space but remarked that it makes generation identification difficult and does not allow navigating to descendants of represented individuals. A set of extensions was proposed to help overcome these limitations and a preliminary evaluation suggested that those extensions enhance the original technique. This paper presents a more thorough evaluation carried out to assess if and how the proposed extensions improve the original h-tree layout pedigree technique. Results suggest that these extensions improve user performance on some tasks, effectively provide new functionality, and generally enhance user experience.","PeriodicalId":354135,"journal":{"name":"2013 17th International Conference on Information Visualisation","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132603608","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}
When using visualisations to explore mathematics, I was seduced by their aesthetics. It led me to see them as a form of art and to start to produce mathematical visualisations just for the sake of it. I began to study the interactions between mathematics and art. At the same time, I discovered that mathematicians have a very special way to look to the world. This paper is an account of my research in the matter.
{"title":"A Mathematical Look to the World","authors":"H. Lehning","doi":"10.1109/IV.2013.48","DOIUrl":"https://doi.org/10.1109/IV.2013.48","url":null,"abstract":"When using visualisations to explore mathematics, I was seduced by their aesthetics. It led me to see them as a form of art and to start to produce mathematical visualisations just for the sake of it. I began to study the interactions between mathematics and art. At the same time, I discovered that mathematicians have a very special way to look to the world. This paper is an account of my research in the matter.","PeriodicalId":354135,"journal":{"name":"2013 17th International Conference on Information Visualisation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132814583","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}
Visual Mining is typically concerned with the visualization of data and its representation to facilitate the mining aiming at extracting interesting and hidden information. It can also mean the visualization of the results of the mining process with the purpose of deepening the understanding of such results and maximizing its exploitation. However, in global systems and global economies, the targeted knowledge of interest may not be embedded in one database or data system. Instead, it may be hidden, not in the data sets, but in the relations between seemingly unrelated data systems. We introduce this problem and the concept of Distributed Relational Visual Mining and its potential for information and knowledge discovery from distributed seemingly disconnected systems. With potential applications in many areas, we introduce a case study applying the proposed technique in the area of Software Development in general and Software testing in Particular.
{"title":"Extracting Hidden Information and Conclusions in Software Testing Via Distributed Relational Visual Mining","authors":"Walaa Akram Anwar, A. Moussa, A. Salah","doi":"10.1109/IV.2013.71","DOIUrl":"https://doi.org/10.1109/IV.2013.71","url":null,"abstract":"Visual Mining is typically concerned with the visualization of data and its representation to facilitate the mining aiming at extracting interesting and hidden information. It can also mean the visualization of the results of the mining process with the purpose of deepening the understanding of such results and maximizing its exploitation. However, in global systems and global economies, the targeted knowledge of interest may not be embedded in one database or data system. Instead, it may be hidden, not in the data sets, but in the relations between seemingly unrelated data systems. We introduce this problem and the concept of Distributed Relational Visual Mining and its potential for information and knowledge discovery from distributed seemingly disconnected systems. With potential applications in many areas, we introduce a case study applying the proposed technique in the area of Software Development in general and Software testing in Particular.","PeriodicalId":354135,"journal":{"name":"2013 17th International Conference on Information Visualisation","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115604047","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}
When evaluating a network topology, occasionally data structures cannot be segmented into absolute, heterogeneous groups. There may be a spectrum to the dataset that does not allow for this hard clustering approach and may need to segment using fuzzy/overlapping communities or cliques. Even to this degree, when group members can belong to multiple cliques, there leaves an ever present layer of doubt, noise, and outliers caused by the overlapping clustering algorithms. These imperfections can either be corrected by an expert user to enhance the clustering algorithm or to preserve their own mental models of the communities. Presented is a visualization that models overlapping community membership and provides an interactive interface to facilitate a quick and efficient means of both sorting through large network topologies and preserving the user's mental model of the structure.
{"title":"A Semi-supervised Approach to Visualizing and Manipulating Overlapping Communities","authors":"Patrick M. Dudas, M. D. Jongh, Peter Brusilovsky","doi":"10.1109/IV.2013.23","DOIUrl":"https://doi.org/10.1109/IV.2013.23","url":null,"abstract":"When evaluating a network topology, occasionally data structures cannot be segmented into absolute, heterogeneous groups. There may be a spectrum to the dataset that does not allow for this hard clustering approach and may need to segment using fuzzy/overlapping communities or cliques. Even to this degree, when group members can belong to multiple cliques, there leaves an ever present layer of doubt, noise, and outliers caused by the overlapping clustering algorithms. These imperfections can either be corrected by an expert user to enhance the clustering algorithm or to preserve their own mental models of the communities. Presented is a visualization that models overlapping community membership and provides an interactive interface to facilitate a quick and efficient means of both sorting through large network topologies and preserving the user's mental model of the structure.","PeriodicalId":354135,"journal":{"name":"2013 17th International Conference on Information Visualisation","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123609010","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}
S. Kimani, Mariano Leva, Massimo Mecella, T. Catarci
Sensors have become increasingly deployed in many areas, and especially in industrial engineering settings. Sensor data is however temporal, massive, and multidimensional in nature. Such characteristics make it difficult to render sensor data for visual analysis. Although there exist potential visualizations for sensor data, there is very little specific guidance in literature on the visualization of sensor data in order to support industrial engineers in decision making. Existing visualizations are not always readily applicable to the domain of industrial engineering. This paper sheds more light on the area and proposes a visualization environment for supporting industrial engineers in their tasks.
{"title":"Visualization of Multidimensional Sensor Data in Industrial Engineering","authors":"S. Kimani, Mariano Leva, Massimo Mecella, T. Catarci","doi":"10.1109/IV.2013.19","DOIUrl":"https://doi.org/10.1109/IV.2013.19","url":null,"abstract":"Sensors have become increasingly deployed in many areas, and especially in industrial engineering settings. Sensor data is however temporal, massive, and multidimensional in nature. Such characteristics make it difficult to render sensor data for visual analysis. Although there exist potential visualizations for sensor data, there is very little specific guidance in literature on the visualization of sensor data in order to support industrial engineers in decision making. Existing visualizations are not always readily applicable to the domain of industrial engineering. This paper sheds more light on the area and proposes a visualization environment for supporting industrial engineers in their tasks.","PeriodicalId":354135,"journal":{"name":"2013 17th International Conference on Information Visualisation","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125222610","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}
In this paper, we present a marker-less AR framework which enables virtual graffiti creation and reference image generation. Our framework also supports 3D annotations such as image textures (virtual graffiti), 3D objects and 3D text, which are superposed over the video stream. We adopt marker-less tracking technique based on key point based descriptors and the trackers. In general, reference image for marker-less AR must be acquired from real image in advance. In such situation, most marker-less tracking approaches force user to capture the front view of a target object. We suppose that reference image does not have to be captured under such condition. In experiments, we showed the estimation accuracy for reference image generation. And we demonstrated real-time marker-less tracking including reference image generation, easy-to-use virtual graffiti creation and immediate superimposing.
{"title":"A Reference Image Generation Method for Marker-less AR","authors":"S. Yonemoto","doi":"10.1109/IV.2013.54","DOIUrl":"https://doi.org/10.1109/IV.2013.54","url":null,"abstract":"In this paper, we present a marker-less AR framework which enables virtual graffiti creation and reference image generation. Our framework also supports 3D annotations such as image textures (virtual graffiti), 3D objects and 3D text, which are superposed over the video stream. We adopt marker-less tracking technique based on key point based descriptors and the trackers. In general, reference image for marker-less AR must be acquired from real image in advance. In such situation, most marker-less tracking approaches force user to capture the front view of a target object. We suppose that reference image does not have to be captured under such condition. In experiments, we showed the estimation accuracy for reference image generation. And we demonstrated real-time marker-less tracking including reference image generation, easy-to-use virtual graffiti creation and immediate superimposing.","PeriodicalId":354135,"journal":{"name":"2013 17th International Conference on Information Visualisation","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127471123","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}
We describe a tool to analyze unlinked documents by visualizing networks extracted using textual analysis. Our focus is on developing an interactive visual analytics tool that enables a user to interactively observe data to detect features of the documents that may be known or unknown in advance. We have tested our tool using two data sets, one consisting of 1000 documents and the other of 360 documents. The resulting visualization showed that our tool provides a simple yet powerful method to identify trends and to find facts in the documents quickly due to its interactivity.
{"title":"Toward Visual Analytics of Unlinked Documents by Textual Analysis and Network Visualization","authors":"B. Shizuki, H. Hosobe","doi":"10.1109/IV.2013.30","DOIUrl":"https://doi.org/10.1109/IV.2013.30","url":null,"abstract":"We describe a tool to analyze unlinked documents by visualizing networks extracted using textual analysis. Our focus is on developing an interactive visual analytics tool that enables a user to interactively observe data to detect features of the documents that may be known or unknown in advance. We have tested our tool using two data sets, one consisting of 1000 documents and the other of 360 documents. The resulting visualization showed that our tool provides a simple yet powerful method to identify trends and to find facts in the documents quickly due to its interactivity.","PeriodicalId":354135,"journal":{"name":"2013 17th International Conference on Information Visualisation","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129144846","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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