2021 25th International Conference Information Visualisation (IV)最新文献_第2页

Visual Analytics to Support Industrial Vehicle Fleet Planning 可视化分析支持工业车队规划

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00020

Guilherme X. Ferreira, Melise M. V. Paula, Rafael Perez Pagan, B. Batista

引用次数: 0

[Copyright notice] (版权)

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/iv53921.2021.00003

引用次数: 0

Towards a Visual Approach for Representing Analytical Provenance in Exploration Processes 探索过程中分析物源的可视化表示方法

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00014

Aline Menin, R. Cava, C. Freitas, O. Corby, M. Winckler

{"title":"Towards a Visual Approach for Representing Analytical Provenance in Exploration Processes","authors":"Aline Menin, R. Cava, C. Freitas, O. Corby, M. Winckler","doi":"10.1109/IV53921.2021.00014","DOIUrl":"https://doi.org/10.1109/IV53921.2021.00014","url":null,"abstract":"Visualization techniques are useful tools to explore data by enabling the discovery of meaningful patterns and causal relationships. The discovery process is often exploratory and requires multiple views to support analyzing different or complementary perspectives to the data. In this context, analytic provenance shows great potential to understand users’ reasoning process through the study of their interactions on multiple view systems. In this paper, we present an approach based on the concept of chained views to support the incremental exploration of large, multidimensional datasets. Our goal is to provide visual representation of provenance information to enable users to retrace their analytical actions and to discover alternative exploratory paths without loosing information on previous analyses. We demonstrate that our implementation of the approach, MGExplorer (Multidimensional Graph Explorer), allows users to explore different perspectives to a dataset by modifying the input graph topology, choosing visualization techniques, arranging the visualization space in meaningful ways to the ongoing analysis and retracing their analytical actions. MGExplorer combines multiple visualization techniques and visual querying while representing provenance information as segments connecting views, which each supports selection operations that help define subsets of the current dataset to be explored by a different view. We demonstrate the usage of the tool through a study case where we explore co-authorship data. We assess the approach through performance metrics, temporal ordering of tasks, number of physical actions, and amount of information to be recalled inbetween actions applied to the chosen visual exploration scenarios using chained views.","PeriodicalId":380260,"journal":{"name":"2021 25th International Conference Information Visualisation (IV)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127848485","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}

引用次数: 6

Localization of Emotion via EEG Analysis using 3D Trilateration 基于脑电分析的三维三边定位

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/iv53921.2021.00039

H. Blaiech, N. Liouane, M. Saafi

引用次数: 0

ExTraVis: Exploration of Traffic Incidents Using a Visual Interactive System 利用视觉交互系统探索交通事故

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00018

Joshua Zerafa, Md. Rafiqul Islam, M. A. Kabir, Guandong Xu

{"title":"ExTraVis: Exploration of Traffic Incidents Using a Visual Interactive System","authors":"Joshua Zerafa, Md. Rafiqul Islam, M. A. Kabir, Guandong Xu","doi":"10.1109/IV53921.2021.00018","DOIUrl":"https://doi.org/10.1109/IV53921.2021.00018","url":null,"abstract":"The impact of road traffic incidents (e.g., road accidents, vehicle breakdowns) have become progressively worse over the years, being a major cause of many adverse issues such as serious injury, economic loss, and lifelong disabilities. Thus, it is essential to acknowledge these issues and proactively construct appropriate solutions to mitigate the impact of these issues in the future. This study outlines the history of traffic incident research and covers several solutions such as machine learning, mathematical modeling, and visualization system to traffic incident analysis. In this paper, we design a unique visualization system, ExTraVis, for incident data exploration and analysis that can be used to help traffic management controllers, aid to make decisions, and help them to understand how past incidents affected and where incidents may occur. The key features of this system are visual exploration and analysis to overcome the problems linked with road traffic incidents and to encourage future work and improvements. Additionally, we gather various custom queries for free text search feature. We find that people ask questions and our system provide 90% correct visual insights. Finally, we demonstrate the effectiveness and robustness of ExTraVis by comparing with three different incident visualization dashboards and a user study.","PeriodicalId":380260,"journal":{"name":"2021 25th International Conference Information Visualisation (IV)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123738254","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}

引用次数: 4

Graph embedding of music structures for machine learning approaches 面向机器学习的音乐结构图嵌入方法

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00054

R. Zaccagnino, Gerardo Benevento, R. De Prisco, Alfonso Guarino, N. Lettieri, Delfina Malandrino

{"title":"Graph embedding of music structures for machine learning approaches","authors":"R. Zaccagnino, Gerardo Benevento, R. De Prisco, Alfonso Guarino, N. Lettieri, Delfina Malandrino","doi":"10.1109/IV53921.2021.00054","DOIUrl":"https://doi.org/10.1109/IV53921.2021.00054","url":null,"abstract":"Several works on representation learning for graph-structured data have been proposed in recent literature. However, most of such techniques have several downsides. On the one hand, graph kernels which use handcrafted features (e.g., shortest paths) are hampered by poor generalization problems. On the other hand, methods for learning representations of whole graphs deal with unattributed or single-attributed graphs.In this work, we propose a novel technique for graph embedding learning able to take into account multi-attribute graphs (from 1 to an arbitrary number). Given a multi-attribute graph, the proposed method generates an embedding vector as follows: (i) the graph is split into several single-attribute graphs; for each of these, one numeric vector is generated by using state-of-the-art graph embedding techniques; (ii) the obtained vectors are concatenated in one representative vector using a multi-view learning integration technique; (iii) the size of such a vector is reduced through deep autoencoders.Experiments have been conducted on the music style recognition problem. We focus on the corpus of 4-voice J. S. Bach’ compositions. First, such a corpus has been decomposed and translated into graph-based structures corresponding to the music scores. Then, the proposed method is applied to generate the embedding vectors from the obtained graphs. Finally, a Random Forest model trained on such obtained vectors is used for generating novels music compositions in the learned style. Results obtained show the effectiveness of the proposed approach.","PeriodicalId":380260,"journal":{"name":"2021 25th International Conference Information Visualisation (IV)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130574214","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}

引用次数: 0

Visualization of sub-network sets by iterative graph sampling from large scale networks 基于大规模网络迭代图采样的子网络集可视化

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/iv53921.2021.00011

Namiko Toriyama, Mitsuo Yoshida, T. Itoh

引用次数: 0

A Visualization Method for Training Data Comparison 训练数据比较的可视化方法

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00040

Karen Kosaka, T. Itoh

引用次数: 0

ContourDiff: Revealing Differential Trends in Spatiotemporal Data ContourDiff:揭示时空数据的差异趋势

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00016

Zonayed Ahmed, Michael Beyene, Debajyoti Mondal, C. Roy, Christopher Dutchyn, Kevin A. Schneider

{"title":"ContourDiff: Revealing Differential Trends in Spatiotemporal Data","authors":"Zonayed Ahmed, Michael Beyene, Debajyoti Mondal, C. Roy, Christopher Dutchyn, Kevin A. Schneider","doi":"10.1109/IV53921.2021.00016","DOIUrl":"https://doi.org/10.1109/IV53921.2021.00016","url":null,"abstract":"Changes in spatiotemporal data may often go unnoticed due to their inherent noise and low variability (e.g., geological processes over years). Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the temporal changes in such data. We propose ContourDiff, a vector-based visualization over contour plots to visualize the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors along the contour paths, revealing differential trends that the contour lines experienced over time. We evaluated our visualization using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data.","PeriodicalId":380260,"journal":{"name":"2021 25th International Conference Information Visualisation (IV)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128025345","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}

引用次数: 2

Visualization Resources: A Starting Point 可视化资源:一个起点

2021 25th International Conference Information Visualisation (IV) Pub Date : 2021-07-01 DOI: 10.1109/IV53921.2021.00034

Xiaoxiao Liu, Mohammad Alharbi, Joe Best, Jian Chen, A. Diehl, Elif E. Firat, Dylan Rees, Qiru Wang, R. Laramee

{"title":"Visualization Resources: A Starting Point","authors":"Xiaoxiao Liu, Mohammad Alharbi, Joe Best, Jian Chen, A. Diehl, Elif E. Firat, Dylan Rees, Qiru Wang, R. Laramee","doi":"10.1109/IV53921.2021.00034","DOIUrl":"https://doi.org/10.1109/IV53921.2021.00034","url":null,"abstract":"Visualization, as a vibrant field for researchers, practitioners, and higher educational institutions, is growing and evolving very rapidly. Tremendous progress has been made since 1987, the year often cited as the beginning of data visualization as a distinct field. As such, the number of visualization resources and the demand for those resources are increasing at a very fast pace. We present a collection of open visualization resources for all those with an interest in interactive data visualization and visual analytics. Because the number of resources is so large, we focus on collections of resources, of which there are already very many ranging from literature collections to collections of practitioner resources. We develop a novel classification of visualization resource collections based on the resource type, e.g. literature-based, web-based, etc. The result is a helpful overview and details-on-demand of many useful resources. The collection offers a valuable jump-start for those seeking out data visualization resources from all backgrounds spanning from beginners such as students to teachers, practitioners, and researchers wishing to create their own advanced or novel visual designs.","PeriodicalId":380260,"journal":{"name":"2021 25th International Conference Information Visualisation (IV)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124500213","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}

引用次数: 6