Pub Date : 2015-04-14DOI: 10.1109/PACIFICVIS.2015.7156387
Aditya Acharya, V. Natarajan
The contour tree is a topological structure associated with a scalar function that tracks the connectivity of the evolving level sets of the function. It supports intuitive and interactive visual exploration and analysis of the scalar function. This paper describes a fast, parallel, and memory efficient algorithm for constructing the contour tree of a scalar function on shared memory systems. Comparisons with existing implementations show significant improvement in both the running time and the memory expended. The proposed algorithm is particularly suited for large datasets that do not fit in memory. For example, the contour tree for a scalar function defined on a 8.6 billion vertex domain (2048×2048×2048 volume data) can be efficiently constructed using less than 10GB of memory.
{"title":"A parallel and memory efficient algorithm for constructing the contour tree","authors":"Aditya Acharya, V. Natarajan","doi":"10.1109/PACIFICVIS.2015.7156387","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2015.7156387","url":null,"abstract":"The contour tree is a topological structure associated with a scalar function that tracks the connectivity of the evolving level sets of the function. It supports intuitive and interactive visual exploration and analysis of the scalar function. This paper describes a fast, parallel, and memory efficient algorithm for constructing the contour tree of a scalar function on shared memory systems. Comparisons with existing implementations show significant improvement in both the running time and the memory expended. The proposed algorithm is particularly suited for large datasets that do not fit in memory. For example, the contour tree for a scalar function defined on a 8.6 billion vertex domain (2048×2048×2048 volume data) can be efficiently constructed using less than 10GB of memory.","PeriodicalId":177381,"journal":{"name":"2015 IEEE Pacific Visualization Symposium (PacificVis)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130558258","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 introduce a novel visualization method which allows people to explore, compare and refine the major communities in a large network. We first detect major communities in a network using data mining and community analysis methods. Then, the statistics attributes of each community, the relational strength between communities, and the boundary nodes connecting those communities are computed and stored. We propose a novel method based on Voronoi treemap to encode each community with a polygon and the relative positions of polygons encode their relational strengths. Different community attributes can be encoded by polygon shapes, sizes and colors. A corner-cutting method is further introduced to adjust the smoothness of polygons based on certain community attribute. To accommodate the boundary nodes, the gaps between the polygons are widened by a polygon-shrinking algorithm such that the boundary nodes can be conveniently embedded into the newly created spaces. The method is very efficient, enabling users to test different community detection algorithms, fine tune the results, and explore the fuzzy relations between communities interactively. The case studies with two real data sets demonstrate that our approach can provide a visual summary of major communities in a large network, and help people better understand the characteristics of each community and inspect various relational patterns between communities.
{"title":"Interactive visual summary of major communities in a large network","authors":"Yanhong Wu, Wenbin Wu, Sixiao Yang, Youliang Yan, Huamin Qu","doi":"10.1109/PACIFICVIS.2015.7156355","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2015.7156355","url":null,"abstract":"In this paper, we introduce a novel visualization method which allows people to explore, compare and refine the major communities in a large network. We first detect major communities in a network using data mining and community analysis methods. Then, the statistics attributes of each community, the relational strength between communities, and the boundary nodes connecting those communities are computed and stored. We propose a novel method based on Voronoi treemap to encode each community with a polygon and the relative positions of polygons encode their relational strengths. Different community attributes can be encoded by polygon shapes, sizes and colors. A corner-cutting method is further introduced to adjust the smoothness of polygons based on certain community attribute. To accommodate the boundary nodes, the gaps between the polygons are widened by a polygon-shrinking algorithm such that the boundary nodes can be conveniently embedded into the newly created spaces. The method is very efficient, enabling users to test different community detection algorithms, fine tune the results, and explore the fuzzy relations between communities interactively. The case studies with two real data sets demonstrate that our approach can provide a visual summary of major communities in a large network, and help people better understand the characteristics of each community and inspect various relational patterns between communities.","PeriodicalId":177381,"journal":{"name":"2015 IEEE Pacific Visualization Symposium (PacificVis)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126502007","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 : 2015-03-16DOI: 10.1109/PACIFICVIS.2015.7156356
Quirijn W. Bouts, B. Speckmann
The classic method to depict graphs is a node-link diagram where vertices (nodes) are associated with each object and edges (links) connect related objects. However, node-link diagrams quickly appear cluttered and unclear, even for moderately sized graphs. If the positions of the nodes are fixed then suitable link routing is the only option to reduce clutter. We present a novel link clustering and routing algorithm which respects (and if desired refines) user-defined clusters on links. If no clusters are defined a priori we cluster based on geometric criteria, that is, based on a well-separated pair decomposition (WSPD).We route link clusters individually on a sparse visibility spanner. To completely avoid ambiguity we draw each individual link and ensure that clustered links follow the same path in the routing graph. We prove that the clusters induced by the WSPD consist of compatible links according to common similarity measures as formalized by Holten and van Wijk [17]. The greedy sparsification of the visibility graph allows us to easily route around obstacles. Our experimental results are visually appealing and convey a sense of abstraction and order.
{"title":"Clustered edge routing","authors":"Quirijn W. Bouts, B. Speckmann","doi":"10.1109/PACIFICVIS.2015.7156356","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2015.7156356","url":null,"abstract":"The classic method to depict graphs is a node-link diagram where vertices (nodes) are associated with each object and edges (links) connect related objects. However, node-link diagrams quickly appear cluttered and unclear, even for moderately sized graphs. If the positions of the nodes are fixed then suitable link routing is the only option to reduce clutter. We present a novel link clustering and routing algorithm which respects (and if desired refines) user-defined clusters on links. If no clusters are defined a priori we cluster based on geometric criteria, that is, based on a well-separated pair decomposition (WSPD).We route link clusters individually on a sparse visibility spanner. To completely avoid ambiguity we draw each individual link and ensure that clustered links follow the same path in the routing graph. We prove that the clusters induced by the WSPD consist of compatible links according to common similarity measures as formalized by Holten and van Wijk [17]. The greedy sparsification of the visibility graph allows us to easily route around obstacles. Our experimental results are visually appealing and convey a sense of abstraction and order.","PeriodicalId":177381,"journal":{"name":"2015 IEEE Pacific Visualization Symposium (PacificVis)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121766128","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 : 2014-10-01DOI: 10.1109/VAST.2014.7042528
Conglei Shi, Siwei Fu, Qing Chen, Huamin Qu
Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. With thousands of students watching course videos, enormous amounts of clickstream data are produced and recorded by the MOOCs platforms for each course. Such large-scale data provide a great opportunity for instructors and educational analysts to gain insight into online learning behaviors on an unprecedented scale. Nevertheless, the growing scale and unique characteristics of the data also pose a special challenge for effective data analysis. In this paper, we introduce VisMOOC, a visual analytic system to help analyze user learning behaviors by using video clickstream data from MOOC platforms. We work closely with the instructors of two Coursera courses to understand the data and collect task analysis requirements. A complete user-centered design process is further employed to design and develop VisMOOC. It includes three main linked views: the List View to show an overview of the clickstream differences among course videos, the Content-based View to show temporal variations in the total number of each type of click action along the video timeline, the Dashboard View to show various statistical information such as demographic information and temporal information. We conduct two case studies with the instructors to demonstrate the usefulness of VisMOOC and discuss new findings on learning behaviors.
{"title":"VisMOOC: Visualizing video clickstream data from Massive Open Online Courses","authors":"Conglei Shi, Siwei Fu, Qing Chen, Huamin Qu","doi":"10.1109/VAST.2014.7042528","DOIUrl":"https://doi.org/10.1109/VAST.2014.7042528","url":null,"abstract":"Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. With thousands of students watching course videos, enormous amounts of clickstream data are produced and recorded by the MOOCs platforms for each course. Such large-scale data provide a great opportunity for instructors and educational analysts to gain insight into online learning behaviors on an unprecedented scale. Nevertheless, the growing scale and unique characteristics of the data also pose a special challenge for effective data analysis. In this paper, we introduce VisMOOC, a visual analytic system to help analyze user learning behaviors by using video clickstream data from MOOC platforms. We work closely with the instructors of two Coursera courses to understand the data and collect task analysis requirements. A complete user-centered design process is further employed to design and develop VisMOOC. It includes three main linked views: the List View to show an overview of the clickstream differences among course videos, the Content-based View to show temporal variations in the total number of each type of click action along the video timeline, the Dashboard View to show various statistical information such as demographic information and temporal information. We conduct two case studies with the instructors to demonstrate the usefulness of VisMOOC and discuss new findings on learning behaviors.","PeriodicalId":177381,"journal":{"name":"2015 IEEE Pacific Visualization Symposium (PacificVis)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132524471","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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