2011 IEEE Conference on Visual Analytics Science and Technology (VAST)最新文献_第3页

Observation-level interaction with statistical models for visual analytics 与可视化分析统计模型的观察级交互

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102449

A. Endert, Chao Han, Dipayan Maiti, L. House, Scotland Leman, Chris North

{"title":"Observation-level interaction with statistical models for visual analytics","authors":"A. Endert, Chao Han, Dipayan Maiti, L. House, Scotland Leman, Chris North","doi":"10.1109/VAST.2011.6102449","DOIUrl":"https://doi.org/10.1109/VAST.2011.6102449","url":null,"abstract":"In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus “observation”) in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools.","PeriodicalId":103998,"journal":{"name":"2011 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116440293","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: 165

KD-photomap: Exploring photographs in space and time KD-photomap:探索空间和时间中的照片

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102479

Iulian Peca, Haolin Zhi, K. Vrotsou, N. Andrienko, G. Andrienko

引用次数: 7

Exploring agent-based simulations using temporal graphs 使用时间图探索基于代理的模拟

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102469

R. J. Crouser, Jeremy G. Freeman, Remco Chang

引用次数: 0

Guiding feature subset selection with an interactive visualization 通过交互式可视化指导特征子集的选择

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102448

T. May, A. Bannach, J. Davey, T. Ruppert, J. Kohlhammer

{"title":"Guiding feature subset selection with an interactive visualization","authors":"T. May, A. Bannach, J. Davey, T. Ruppert, J. Kohlhammer","doi":"10.1109/VAST.2011.6102448","DOIUrl":"https://doi.org/10.1109/VAST.2011.6102448","url":null,"abstract":"We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset selection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features.","PeriodicalId":103998,"journal":{"name":"2011 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132576029","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: 97

Geovisual analytics for cyber security: Adopting the GeoViz Toolkit 网络安全的地理可视化分析:采用GeoViz工具包

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102491

N. Giacobe, Sen Xu

引用次数: 21

Interactive data analysis with nSpace2® 交互式数据分析与nSpace2®

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102497

C. Canfield, D. Sheffield

引用次数: 2

An integrated visualization on network events VAST 2011 Mini Challenge #2 Award: “Outstanding integrated overview display” 2011迷你挑战赛第二名:“杰出综合概览展示”

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102493

Walter Marcelo Lamagna

引用次数: 6

Pointwise local pattern exploration for sensitivity analysis 灵敏度分析的点向局部模式探索

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102450

Zhenyu Guo, M. Ward, Elke A. Rundensteiner, Carolina Ruiz

{"title":"Pointwise local pattern exploration for sensitivity analysis","authors":"Zhenyu Guo, M. Ward, Elke A. Rundensteiner, Carolina Ruiz","doi":"10.1109/VAST.2011.6102450","DOIUrl":"https://doi.org/10.1109/VAST.2011.6102450","url":null,"abstract":"Sensitivity analysis is a powerful method for discovering the significant factors that contribute to targets and understanding the interaction between variables in multivariate datasets. A number of sensitivity analysis methods fall into the class of local analysis, in which the sensitivity is defined as the partial derivatives of a target variable with respect to a group of independent variables. Incorporating sensitivity analysis in visual analytic tools is essential for multivariate phenomena analysis. However, most current multivariate visualization techniques do not allow users to explore local patterns individually for understanding the sensitivity from a pointwise view. In this paper, we present a novel pointwise local pattern exploration system for visual sensitivity analysis. Using this system, analysts are able to explore local patterns and the sensitivity at individual data points, which reveals the relationships between a focal point and its neighbors. During exploration, users are able to interactively change the derivative coefficients to perform sensitivity analysis based on different requirements as well as their domain knowledge. Each local pattern is assigned an outlier factor, so that users can quickly identify anomalous local patterns that do not conform with the global pattern. Users can also compare the local pattern with the global pattern both visually and statistically. Finally, the local pattern is integrated into the original attribute space using color mapping and jittering, which reveals the distribution of the partial derivatives. Case studies with real datasets are used to investigate the effectiveness of the visualizations and interactions.","PeriodicalId":103998,"journal":{"name":"2011 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123109566","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: 22

Visual analytics of terrorist activities related to epidemics 与流行病有关的恐怖主义活动的可视化分析

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102498

E. Bertini, J. Buchmüller, Fabian Fischer, Stephan Huber, Thomas Lindemeier, Fabian Maass, Florian Mansmann, T. Ramm, Michael Regenscheit, Christian Rohrdantz, Christian Scheible, T. Schreck, Stephan Sellien, Florian Stoffel, Mark Tautzenberger, Matthias Zieker, D. Keim

引用次数: 11

Characterizing the intelligence analysis process: Informing visual analytics design through a longitudinal field study 表征智能分析过程:通过纵向实地研究为视觉分析设计提供信息

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102438

Y. Kang, J. Stasko

引用次数: 98