Pub Date : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475466
Martin Suntinger, Hannes Obweger, Josef Schiefer, E. Gröller
Event-based systems are gaining increasing popularity for building loosely coupled and distributed systems. Since business processes are becoming more interconnected and event-driven, event-based systems fit well for supporting and monitoring business processes. In this paper, we present an event-based business intelligence tool, the Event Tunnel framework. It provides an interactive visualization of event streams to support business analysts in exploring business incidents. The visualization is based on the metaphor of considering the event stream as a cylindrical tunnel, which is presented to the user from multiple perspectives. The information of single events laid out in the Event Tunnel is encoded in event glyphs that allow for a selective mapping of event attributes to colors, size and position. Different policies for the placement of the events in the tunnel as well as a clustering mechanism generate various views on historical event data. The Event Tunnel is able to display the relationships between events. This facilitates users to discover root causes and causal dependencies of event patterns. Our framework couples the event-tunnel visualization with query tools that allow users to search for relevant events within a data repository. Using query, filler and highlighting operations the analyst can navigate through the Event Tunnel until the required information or event patterns become visible. We demonstrate our approach with use cases from the fraud management and logistics domain.
{"title":"The Event Tunnel: Interactive Visualization of Complex Event Streams for Business Process Pattern Analysis","authors":"Martin Suntinger, Hannes Obweger, Josef Schiefer, E. Gröller","doi":"10.1109/PACIFICVIS.2008.4475466","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475466","url":null,"abstract":"Event-based systems are gaining increasing popularity for building loosely coupled and distributed systems. Since business processes are becoming more interconnected and event-driven, event-based systems fit well for supporting and monitoring business processes. In this paper, we present an event-based business intelligence tool, the Event Tunnel framework. It provides an interactive visualization of event streams to support business analysts in exploring business incidents. The visualization is based on the metaphor of considering the event stream as a cylindrical tunnel, which is presented to the user from multiple perspectives. The information of single events laid out in the Event Tunnel is encoded in event glyphs that allow for a selective mapping of event attributes to colors, size and position. Different policies for the placement of the events in the tunnel as well as a clustering mechanism generate various views on historical event data. The Event Tunnel is able to display the relationships between events. This facilitates users to discover root causes and causal dependencies of event patterns. Our framework couples the event-tunnel visualization with query tools that allow users to search for relevant events within a data repository. Using query, filler and highlighting operations the analyst can navigate through the Event Tunnel until the required information or event patterns become visible. We demonstrate our approach with use cases from the fraud management and logistics domain.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115959968","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 : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475456
Markus Chimani, M. Jünger, Michael Schulz
We define the concept of crossing numbers for simultaneous graphs by extending the crossing number problem of traditional graphs. We discuss differences to the traditional crossing number problem, and give an NP-completeness proof and lower and upper bounds for the new problem. Furthermore, we show how existing heuristic and exact algorithms for the traditional problem can be adapted to the new task of simultaneous crossing minimization, and report on a brief experimental study of their implementations.
{"title":"Crossing Minimization meets Simultaneous Drawing","authors":"Markus Chimani, M. Jünger, Michael Schulz","doi":"10.1109/PACIFICVIS.2008.4475456","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475456","url":null,"abstract":"We define the concept of crossing numbers for simultaneous graphs by extending the crossing number problem of traditional graphs. We discuss differences to the traditional crossing number problem, and give an NP-completeness proof and lower and upper bounds for the new problem. Furthermore, we show how existing heuristic and exact algorithms for the traditional problem can be adapted to the new task of simultaneous crossing minimization, and report on a brief experimental study of their implementations.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115281003","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 : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475481
C. Muelder, K. Ma
Abstract graphs or networks are a commonly recurring data type in many fields. In order to visualize such graphs effectively, the graph must be laid out on the screen coherently. Many algorithms exist to do this, but many of these algorithms tend to be very slow when the input graph is large. This paper presents a new approach to the large graph layout problem, which quickly generates an effective layout. This new method proceeds by generating a clustering hierarchy for the graph, applying a treemap to this hierarchy, and finally placing the graph vertices in their associated regions in the treemap. It is ideal for interactive systems where operations such as semantic zooming are to be performed, since most of the work is done in the initial hierarchy calculation, and it takes very little work to recalculate the layout. This method is also valuable in that the resulting layout can be used as the input to an iterative algorithm (e.g., a force directed method), which greatly reduces the number of iterations required to converge to a near optimal layout.
{"title":"A Treemap Based Method for Rapid Layout of Large Graphs","authors":"C. Muelder, K. Ma","doi":"10.1109/PACIFICVIS.2008.4475481","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475481","url":null,"abstract":"Abstract graphs or networks are a commonly recurring data type in many fields. In order to visualize such graphs effectively, the graph must be laid out on the screen coherently. Many algorithms exist to do this, but many of these algorithms tend to be very slow when the input graph is large. This paper presents a new approach to the large graph layout problem, which quickly generates an effective layout. This new method proceeds by generating a clustering hierarchy for the graph, applying a treemap to this hierarchy, and finally placing the graph vertices in their associated regions in the treemap. It is ideal for interactive systems where operations such as semantic zooming are to be performed, since most of the work is done in the initial hierarchy calculation, and it takes very little work to recalculate the layout. This method is also valuable in that the resulting layout can be used as the input to an iterative algorithm (e.g., a force directed method), which greatly reduces the number of iterations required to converge to a near optimal layout.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129445026","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 : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475459
Hong Zhou, Xiaoru Yuan, Weiwei Cui, Huamin Qu, Baoquan Chen
Effectively visualizing complex node-link graphs which depict relationships among data nodes is a challenging task due to the clutter and occlusion resulting from an excessive amount of edges. In this paper, we propose a novel energy-based hierarchical edge clustering method for node-link graphs. Taking into the consideration of the graph topology, our method first samples graph edges into segments using Delaunay triangulation to generate the control points, which are then hierarchically clustered by energy-based optimization. The edges are grouped according to their positions and directions to improve comprehensibility through abstraction and thus reduce visual clutter. The experimental results demonstrate the effectiveness of our proposed method in clustering edges and providing good high level abstractions of complex graphs.
{"title":"Energy-Based Hierarchical Edge Clustering of Graphs","authors":"Hong Zhou, Xiaoru Yuan, Weiwei Cui, Huamin Qu, Baoquan Chen","doi":"10.1109/PACIFICVIS.2008.4475459","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475459","url":null,"abstract":"Effectively visualizing complex node-link graphs which depict relationships among data nodes is a challenging task due to the clutter and occlusion resulting from an excessive amount of edges. In this paper, we propose a novel energy-based hierarchical edge clustering method for node-link graphs. Taking into the consideration of the graph topology, our method first samples graph edges into segments using Delaunay triangulation to generate the control points, which are then hierarchically clustered by energy-based optimization. The edges are grouped according to their positions and directions to improve comprehensibility through abstraction and thus reduce visual clutter. The experimental results demonstrate the effectiveness of our proposed method in clustering edges and providing good high level abstractions of complex graphs.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126772142","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 : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475452
Steven Martin, Han-Wei Shen, R. Samtaney
We present a technique for memory-efficient and time-efficient volume rendering of curvilinear adaptive mesh refinement data defined within extrudable computational spaces. One of the main challenges in the ray casting of curvilinear volumes is that a linear viewing ray in physical space will typically correspond to a curved ray in computational space. The proposed method utilizes a specialized representation of curvilinear space that provides for the compact representation of parameters for transformations between computational space and physical space, without requiring extensive preprocessing. By simplifying the representation of computational space positions using an extrusion of a profile surface, the requisite transformations can be greatly simplified. Our implementation achieves interactive rates with minimal load time and memory overhead using commodity graphics hardware with real-world data.
{"title":"Efficient Rendering of Extrudable Curvilinear Volumes","authors":"Steven Martin, Han-Wei Shen, R. Samtaney","doi":"10.1109/PACIFICVIS.2008.4475452","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475452","url":null,"abstract":"We present a technique for memory-efficient and time-efficient volume rendering of curvilinear adaptive mesh refinement data defined within extrudable computational spaces. One of the main challenges in the ray casting of curvilinear volumes is that a linear viewing ray in physical space will typically correspond to a curved ray in computational space. The proposed method utilizes a specialized representation of curvilinear space that provides for the compact representation of parameters for transformations between computational space and physical space, without requiring extensive preprocessing. By simplifying the representation of computational space positions using an extrusion of a profile surface, the requisite transformations can be greatly simplified. Our implementation achieves interactive rates with minimal load time and memory overhead using commodity graphics hardware with real-world data.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122444951","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 : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475465
Tanasai Sucontphunt, Xiaoru Yuan, Qing Li, Z. Deng
Facial emotions and expressive facial motions have become an intrinsic part of many graphics systems and human computer interaction applications. The dynamics and high dimensionality of facial motion data make its exploration and processing challenging. In this paper, we propose a novel visualization system for expressive facial motion data exploration. Based on Principal Component Analysis (PCA) dimensionality reduction on anatomical facial sub regions, high dimensional facial motion data is mapped to 3D spaces. We further rendered it as colored 3D trajectories and color represents different emotion. We design an intuitive interface to allow users effectively explore and analyze high dimensional facial motion spaces. The applications of our visualization system on novel facial motion synthesis and emotion recognition are demonstrated.
{"title":"A Novel Visualization System for Expressive Facial Motion Data Exploration","authors":"Tanasai Sucontphunt, Xiaoru Yuan, Qing Li, Z. Deng","doi":"10.1109/PACIFICVIS.2008.4475465","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475465","url":null,"abstract":"Facial emotions and expressive facial motions have become an intrinsic part of many graphics systems and human computer interaction applications. The dynamics and high dimensionality of facial motion data make its exploration and processing challenging. In this paper, we propose a novel visualization system for expressive facial motion data exploration. Based on Principal Component Analysis (PCA) dimensionality reduction on anatomical facial sub regions, high dimensional facial motion data is mapped to 3D spaces. We further rendered it as colored 3D trajectories and color represents different emotion. We design an intuitive interface to allow users effectively explore and analyze high dimensional facial motion spaces. The applications of our visualization system on novel facial motion synthesis and emotion recognition are demonstrated.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115905368","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 : 2008-03-05DOI: 10.1109/PACIFICVIS.2008.4475473
E. D. Giacomo, W. Didimo, L. Grilli, G. Liotta, P. Palladino
The paper describes WhatsOnWeb+, a search clustering engine that allows users to browse and analyze the results of a query by means of enhanced graph visualization techniques. WhatsOnWeb+ integrates a wide array of visual interfaces, animation and interaction functionalities, and clustering technologies. The effectiveness of the different visual interfaces and of the different clustering algorithms implemented in the system has been measured by means of an extensive experimental analysis. The described system represents a significant evolution of a previous clustering engine for the Web.
{"title":"WhatsOnWeb+ : An Enhanced Visual Search Clustering Engine","authors":"E. D. Giacomo, W. Didimo, L. Grilli, G. Liotta, P. Palladino","doi":"10.1109/PACIFICVIS.2008.4475473","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475473","url":null,"abstract":"The paper describes WhatsOnWeb+, a search clustering engine that allows users to browse and analyze the results of a query by means of enhanced graph visualization techniques. WhatsOnWeb+ integrates a wide array of visual interfaces, animation and interaction functionalities, and clustering technologies. The effectiveness of the different visual interfaces and of the different clustering algorithms implemented in the system has been measured by means of an extensive experimental analysis. The described system represents a significant evolution of a previous clustering engine for the Web.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123205464","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 : 2008-03-01DOI: 10.1109/PACIFICVIS.2008.4475479
N. Elmqvist, Thanh-Nghi Do, H. Goodell, N. Riche, Jean-Daniel Fekete
We present the zoomable adjacency matrix explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from an overview to the most detailed views. Several components work together in the ZAME tool to make this possible. Efficient matrix ordering algorithms group related elements. Individual data cases are aggregated into higher-order meta-representations. Aggregates are arranged into a pyramid hierarchy that allows for on-demand paging to GPU shader programs to support smooth multiscale browsing. Using ZAME, we are able to explore the entire French Wikipedia - over 500,000 articles and 6,000,000 links - with interactive performance on standard consumer-level computer hardware.
{"title":"ZAME: Interactive Large-Scale Graph Visualization","authors":"N. Elmqvist, Thanh-Nghi Do, H. Goodell, N. Riche, Jean-Daniel Fekete","doi":"10.1109/PACIFICVIS.2008.4475479","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475479","url":null,"abstract":"We present the zoomable adjacency matrix explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from an overview to the most detailed views. Several components work together in the ZAME tool to make this possible. Efficient matrix ordering algorithms group related elements. Individual data cases are aggregated into higher-order meta-representations. Aggregates are arranged into a pyramid hierarchy that allows for on-demand paging to GPU shader programs to support smooth multiscale browsing. Using ZAME, we are able to explore the entire French Wikipedia - over 500,000 articles and 6,000,000 links - with interactive performance on standard consumer-level computer hardware.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115669608","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 : 2008-01-16DOI: 10.1109/PACIFICVIS.2008.4475478
Steve Haroz, K. Ma, K. Heitmann
Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables.
{"title":"Multiple Uncertainties in Time-Variant Cosmological Particle Data","authors":"Steve Haroz, K. Ma, K. Heitmann","doi":"10.1109/PACIFICVIS.2008.4475478","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2008.4475478","url":null,"abstract":"Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128470484","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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