@conference {13766,
	title = {Evaluating visual and statistical exploration of scientific literature networks},
	booktitle = {2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)},
	year = {2011},
	month = {2011/09/18/22},
	pages = {217 - 224},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Action Science Explorer (ASE) is a tool designed to support users in rapidly generating readily consumable summaries of academic literature. It uses citation network visualization, ranking and filtering papers by network statistics, and automatic clustering and summarization techniques. We describe how early formative evaluations of ASE led to a mature system evaluation, consisting of an in-depth empirical evaluation with four domain experts. The evaluation tasks were of two types: predefined tasks to test system performance in common scenarios, and user-defined tasks to test the system{\textquoteright}s usefulness for custom exploration goals. The primary contribution of this paper is a validation of the ASE design and recommendations to provide: easy-to-understand metrics for ranking and filtering documents, user control over which document sets to explore, and overviews of the document set in coordinated views along with details-on-demand of specific papers. We contribute a taxonomy of features for literature search and exploration tools and describe exploration goals identified by our participants.},
	keywords = {abstracting, academic literature, action science explorer, automatic clustering, citation analysis, citation network visualization, Communities, Context, custom exploration goal, Data visualization, Databases, Document filtering, document handling, document ranking, easy-to-understand metrics, empirical evaluation, Google, Graphical user interfaces, Information filtering, Information Visualization, Libraries, literature exploration, network statistics, paper filtering, paper ranking, scientific literature network, statistical exploration, summarization technique, user-defined tasks, visual exploration, Visualization},
	isbn = {978-1-4577-1246-3},
	doi = {10.1109/VLHCC.2011.6070403},
	author = {Gove,R. and Dunne,C. and Shneiderman, Ben and Klavans,J. and Dorr, Bonnie J}
}
@conference {17144,
	title = {EventGraphs: Charting Collections of Conference Connections},
	booktitle = {2011 44th Hawaii International Conference on System Sciences (HICSS)},
	year = {2011},
	month = {2011/01/04/7},
	pages = {1 - 10},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {EventGraphs are social media network diagrams of conversations related to events, such as conferences. Many conferences now communicate a common "hashtag" or keyword to identify messages related to the event. EventGraphs help make sense of the collections of connections that form when people follow, reply or mention one another and a keyword. This paper defines EventGraphs, characterizes different types, and shows how the social media network analysis add-in NodeXL supports their creation and analysis. The structural patterns to look for in EventGraphs are highlighted and design ideas for their improvement are discussed.},
	keywords = {conference connections, Conferences, Data visualization, EventGraphs, hashtag, measurement, Media, message identification, multimedia computing, NodeXL, Real time systems, social media network diagrams, social networking (online), Twitter},
	isbn = {978-1-4244-9618-1},
	doi = {10.1109/HICSS.2011.196},
	author = {Hansen,D. and Smith,M. A and Shneiderman, Ben}
}
@conference {17442,
	title = {TreeVersity: Comparing tree structures by topology and node{\textquoteright}s attributes differences},
	booktitle = {2011 IEEE Conference on Visual Analytics Science and Technology (VAST)},
	year = {2011},
	month = {2011/10/23/28},
	pages = {275 - 276},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {It is common to classify data in hierarchies, they provide a comprehensible way of understanding big amounts of data. From budgets to organizational charts or even the stock market, trees are everywhere and people find them easy to use. However when analysts need to compare two versions of the same tree structure, or two related taxonomies, the task is not so easy. Much work has been done on this topic, but almost all of it has been restricted to either compare the trees by topology, or by the node attribute values. With this project we are proposing TreeVersity, a framework for comparing tree structures, both by structural changes and by differences in the node attributes. This paper is based on our previous work on comparing traffic agencies using LifeFlow [1, 2] and on a first prototype of TreeVersity.},
	keywords = {Computer science, data classification, Data visualization, Educational institutions, hierarchy, Image color analysis, LifeFlow, node attributes differences, Pattern classification, structural changes, Topology, topology attributes differences, traffic agencies, tree structures comparison, trees (mathematics), TreeVersity, Vegetation, Visualization},
	isbn = {978-1-4673-0015-5},
	doi = {10.1109/VAST.2011.6102471},
	author = {Gomez,J.A.G. and Buck-Coleman,A. and Plaisant, Catherine and Shneiderman, Ben}
}
@conference {17484,
	title = {Visual Analysis of Temporal Trends in Social Networks Using Edge Color Coding and Metric Timelines},
	booktitle = {Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)},
	year = {2011},
	month = {2011/10/09/11},
	pages = {549 - 554},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present Net EvViz, a visualization tool for analysis and exploration of a dynamic social network. There are plenty of visual social network analysis tools but few provide features for visualization of dynamically changing networks featuring the addition or deletion of nodes or edges. Our tool extends the code base of the Node XL template for Microsoft Excel, a popular network visualization tool. The key features of this work are (1) The ability of the user to specify and edit temporal annotations to the network components in an Excel sheet, (2) See the dynamics of the network with multiple graph metrics plotted over the time span of the graph, called the Timeline, and (3) Temporal exploration of the network layout using an edge coloring scheme and a dynamic Time slider. The objectives of the new features presented in this paper are to let the data analysts, computer scientists and others to observe the dynamics or evolution in a network interactively. We presented Net EvViz to five users of Node XL and received positive responses.},
	keywords = {Color, computer scientists, data analysts, data visualisation, Data visualization, dynamic social network, dynamic timeslider, edge color coding, excel sheet, Image coding, image colour analysis, Layout, measurement, metric timelines, Microsoft excel, multiple graph metrics, Net EvViz, network components, network layout, network visualization tool, NodeXL template, social networking (online), temporal trends, Twitter, Visualization},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.212},
	author = {Khurana,U. and Nguyen,Viet-An and Hsueh-Chien Cheng and Ahn,Jae-wook and Chen,Xi and Shneiderman, Ben}
}
@article {17487,
	title = {Visual Exploration across Biomedical Databases},
	journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
	volume = {8},
	year = {2011},
	month = {2011/04//March},
	pages = {536 - 550},
	abstract = {Though biomedical research often draws on knowledge from a wide variety of fields, few visualization methods for biomedical data incorporate meaningful cross-database exploration. A new approach is offered for visualizing and exploring a query-based subset of multiple heterogeneous biomedical databases. Databases are modeled as an entity-relation graph containing nodes (database records) and links (relationships between records). Users specify a keyword search string to retrieve an initial set of nodes, and then explore intra- and interdatabase links. Results are visualized with user-defined semantic substrates to take advantage of the rich set of attributes usually present in biomedical data. Comments from domain experts indicate that this visualization method is potentially advantageous for biomedical knowledge exploration.},
	keywords = {Bioinformatics, Biomedical computing, biomedical databases, cross-database exploration, Data exploration and discovery, Data visualization, database management systems, Databases, Factual, DNA, graph theory, Information Storage and Retrieval, information visualization., Keyword search, medical computing, natural language processing, Proteins, semantic networks, semantics, sequences, text mining, User-Computer Interface, user-defined semantics, visual databases},
	isbn = {1545-5963},
	doi = {10.1109/TCBB.2010.1},
	author = {Lieberman,M.D. and Taheri, S. and Guo,Huimin and Mirrashed,F. and Yahav,I. and Aris,A. and Shneiderman, Ben}
}
@conference {17168,
	title = {First Steps to Netviz Nirvana: Evaluating Social Network Analysis with NodeXL},
	booktitle = {International Conference on Computational Science and Engineering, 2009. CSE {\textquoteright}09},
	volume = {4},
	year = {2009},
	month = {2009/08/29/31},
	pages = {332 - 339},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Social Network Analysis (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners and students alike. NodeXL, an open-source template for Microsoft Excel, integrates a library of common network metrics and graph layout algorithms within the familiar spreadsheet format, offering a potentially low-barrier-to-entry framework for teaching and learning SNA. We present the preliminary findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. The majority of students, while information professionals, had little technical background or experience with SNA techniques. Six of the participants had more technical backgrounds and were chosen specifically for their experience with graph drawing and information visualization. Our primary objectives were (1) to evaluate NodeXL as an SNA tool for a broad base of users and (2) to explore methods for teaching SNA. Our complementary dual case-study format demonstrates the usability of NodeXL for a diverse set of users, and significantly, the power of a tightly integrated metrics/visualization tool to spark insight and facilitate sense-making for students of SNA.},
	keywords = {Computer science, computer science education, data visualisation, Data visualization, Educational institutions, graph drawing, graph layout algorithm, Information services, Information Visualization, Internet, Libraries, Microsoft Excel open-source template, MILC, multi-dimensional in-depth long-term case studies, Netviz Nirvana, NodeXL, Open source software, Programming profession, SNA, social network analysis, Social network services, social networking (online), spreadsheet programs, structural relationship, teaching, visual analytics, visualization tool, Web sites},
	isbn = {978-1-4244-5334-4},
	doi = {10.1109/CSE.2009.120},
	author = {Bonsignore,E. M and Dunne,C. and Rotman,D. and Smith,M. and Capone,T. and Hansen,D. L and Shneiderman, Ben}
}
@article {17238,
	title = {Integrating Statistics and Visualization for Exploratory Power: From Long-Term Case Studies to Design Guidelines},
	journal = {IEEE Computer Graphics and Applications},
	volume = {29},
	year = {2009},
	month = {2009/06//May},
	pages = {39 - 51},
	abstract = {Evaluating visual-analytics systems is challenging because laboratory-based controlled experiments might not effectively represent analytical tasks. One such system, Social Action, integrates statistics and visualization in an interactive exploratory tool for social network analysis. This article describes results from long-term case studies with domain experts and extends established design goals for information visualization.},
	keywords = {case studies, Control systems, Data analysis, data mining, data visualisation, Data visualization, data-mining, design guidelines, Employment, exploration, Filters, Guidelines, Information Visualization, insights, laboratory-based controlled experiments, Performance analysis, social network analysis, Social network services, social networking (online), social networks, SocialAction, statistical analysis, Statistics, visual analytics, visual-analytics systems, Visualization},
	isbn = {0272-1716},
	doi = {10.1109/MCG.2009.44},
	author = {Perer,A. and Shneiderman, Ben}
}
@article {17397,
	title = {Temporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {15},
	year = {2009},
	month = {2009/12//Nov},
	pages = {1049 - 1056},
	abstract = {When analyzing thousands of event histories, analysts often want to see the events as an aggregate to detect insights and generate new hypotheses about the data. An analysis tool must emphasize both the prevalence and the temporal ordering of these events. Additionally, the analysis tool must also support flexible comparisons to allow analysts to gather visual evidence. In a previous work, we introduced align, rank, and filter (ARF) to accentuate temporal ordering. In this paper, we present temporal summaries, an interactive visualization technique that highlights the prevalence of event occurrences. Temporal summaries dynamically aggregate events in multiple granularities (year, month, week, day, hour, etc.) for the purpose of spotting trends over time and comparing several groups of records. They provide affordances for analysts to perform temporal range filters. We demonstrate the applicability of this approach in two extensive case studies with analysts who applied temporal summaries to search, filter, and look for patterns in electronic health records and academic records.},
	keywords = {Aggregates, Collaborative work, Computational Biology, Computer Graphics, Data analysis, data visualisation, Data visualization, Databases, Factual, Displays, Event detection, Filters, Heparin, History, Human computer interaction, Human-computer interaction, HUMANS, Information Visualization, Interaction design, interactive visualization technique, Medical Records Systems, Computerized, Pattern Recognition, Automated, Performance analysis, Springs, temporal categorical data visualization, temporal categorical searching, temporal ordering, temporal summaries, Thrombocytopenia, Time factors},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2009.187},
	author = {Wang,T. D and Plaisant, Catherine and Shneiderman, Ben and Spring, Neil and Roseman,D. and Marchand,G. and Mukherjee,V. and Smith,M.}
}
@conference {17453,
	title = {Understanding social computing participation with visual exploration tools},
	booktitle = {International Symposium on Collaborative Technologies and Systems, 2009. CTS {\textquoteright}09},
	year = {2009},
	month = {2009/05/18/22},
	pages = {xi-xii - xi-xii},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The rapid growth of socio-technical systems, social media and social networking websites has raised the importance of understanding the determinants of their success. The pressure to understand success is increased by the shift from playful discretionary applications to mission critical applications in government, business, and civic settings. These include homeland defense, energy sustainability, environmental conservation, disaster response, and community safety. Information visualization tools and statistical methods can both be helpful, but their utility grows when they are well-integrated. This talk will demonstrate novel tools for network evolution and offer a framework for thinking about motivating technology-mediated social participation.},
	keywords = {Application software, Books, Collaborative tools, Computer science, Data visualization, Educational institutions, History, International collaboration, Social network services, Sociotechnical systems},
	isbn = {978-1-4244-4584-4},
	doi = {10.1109/CTS.2009.5067426},
	author = {Shneiderman, Ben}
}
@conference {17360,
	title = {Similarity-Based Forecasting with Simultaneous Previews: A River Plot Interface for Time Series Forecasting},
	booktitle = {Information Visualization, 2007. IV {\textquoteright}07. 11th International Conference},
	year = {2007},
	month = {2007/07/04/6},
	pages = {191 - 196},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Time-series forecasting has a large number of applications. Users with a partial time series for auctions, new stock offerings, or industrial processes desire estimates of the future behavior. We present a data driven forecasting method and interface called similarity-based forecasting (SBF). A pattern matching search in an historical time series dataset produces a subset of curves similar to the partial time series. The forecast is displayed graphically as a river plot showing statistical information about the SBF subset. A forecasting preview interface allows users to interactively explore alternative pattern matching parameters and see multiple forecasts simultaneously. User testing with 8 users demonstrated advantages and led to improvements.},
	keywords = {data driven forecasting method, data visualisation, Data visualization, Economic forecasting, forecasting preview interface, Graphical user interfaces, historical time series dataset, Laboratories, new stock offerings, partial time series, pattern matching, pattern matching search, Predictive models, river plot interface, Rivers, similarity-based forecasting, Smoothing methods, Technological innovation, Testing, time series, time series forecasting, Weather forecasting},
	isbn = {0-7695-2900-3},
	doi = {10.1109/IV.2007.101},
	author = {Buono,P. and Plaisant, Catherine and Simeone,A. and Aris,A. and Shneiderman, Ben and Shmueli,G. and Jank,W.}
}
@article {16983,
	title = {Balancing Systematic and Flexible Exploration of Social Networks},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {12},
	year = {2006},
	month = {2006/10//Sept},
	pages = {693 - 700},
	abstract = {Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks},
	keywords = {Aggregates, algorithms, attribute ranking, Cluster Analysis, Computer Graphics, Computer simulation, Coordinate measuring machines, coordinated views, Data analysis, data visualisation, Data visualization, exploratory data analysis, Filters, Gain measurement, graph theory, Graphical user interfaces, Information Storage and Retrieval, interactive graph visualization, matrix algebra, matrix overview, Models, Biological, Navigation, network visualization, Pattern analysis, Population Dynamics, Social Behavior, social network analysis, Social network services, social networks, social sciences computing, Social Support, SocialAction, software, statistical analysis, statistical methods, User-Computer Interface},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2006.122},
	author = {Perer,A. and Shneiderman, Ben}
}
@article {17272,
	title = {Knowledge discovery in high-dimensional data: case studies and a user survey for the rank-by-feature framework},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {12},
	year = {2006},
	month = {2006/06//May},
	pages = {311 - 322},
	abstract = {Knowledge discovery in high-dimensional data is a challenging enterprise, but new visual analytic tools appear to offer users remarkable powers if they are ready to learn new concepts and interfaces. Our three-year effort to develop versions of the hierarchical clustering explorer (HCE) began with building an interactive tool for exploring clustering results. It expanded, based on user needs, to include other potent analytic and visualization tools for multivariate data, especially the rank-by-feature framework. Our own successes using HCE provided some testimonial evidence of its utility, but we felt it necessary to get beyond our subjective impressions. This paper presents an evaluation of the hierarchical clustering explorer (HCE) using three case studies and an e-mail user survey (n=57) to focus on skill acquisition with the novel concepts and interface for the rank-by-feature framework. Knowledgeable and motivated users in diverse fields provided multiple perspectives that refined our understanding of strengths and weaknesses. A user survey confirmed the benefits of HCE, but gave less guidance about improvements. Both evaluations suggested improved training methods},
	keywords = {case study, Computer aided software engineering, Computer Society, Data analysis, data mining, data visualisation, Data visualization, database management systems, e-mail user survey, Genomics, Helium, Hierarchical Clustering Explorer, hierarchical clustering explorer., high-dimensional data, Histograms, Information visualization evaluation, interactive systems, interactive tool, knowledge discovery, multivariate data, Rank-by-feature framework, Scattering, Testing, user interface, User interfaces, user survey, visual analytic tools, visual analytics, visualization tools},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2006.50},
	author = {Seo,Jinwook and Shneiderman, Ben}
}
@article {17307,
	title = {Network Visualization by Semantic Substrates},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {12},
	year = {2006},
	month = {2006/10//Sept},
	pages = {733 - 740},
	abstract = {Networks have remained a challenge for information visualization designers because of the complex issues of node and link layout coupled with the rich set of tasks that users present. This paper offers a strategy based on two principles: (1) layouts are based on user-defined semantic substrates, which are non-overlapping regions in which node placement is based on node attributes, (2) users interactively adjust sliders to control link visibility to limit clutter and thus ensure comprehensibility of source and destination. Scalability is further facilitated by user control of which nodes are visible. We illustrate our semantic substrates approach as implemented in NVSS 1.0 with legal precedent data for up to 1122 court cases in three regions with 7645 legal citations},
	keywords = {Automatic control, data visualisation, Data visualization, Displays, Filters, Graphical user interfaces, Information Visualization, information visualization designers, Law, legal citations, Legal factors, legal precedent data, network visualization, NVSS 1.0, scalability, semantic substrate, Terminology, Tunneling, user-defined semantic substrates},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2006.166},
	author = {Shneiderman, Ben and Aris,A.}
}
@conference {16951,
	title = {A Visual Interface for Multivariate Temporal Data: Finding Patterns of Events across Multiple Histories},
	booktitle = {Visual Analytics Science And Technology, 2006 IEEE Symposium On},
	year = {2006},
	month = {2006/11/31/Oct. },
	pages = {167 - 174},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Finding patterns of events over time is important in searching patient histories, Web logs, news stories, and criminal activities. This paper presents PatternFinder, an integrated interface for query and result-set visualization for search and discovery of temporal patterns within multivariate and categorical data sets. We define temporal patterns as sequences of events with inter-event time spans. PatternFinder allows users to specify the attributes of events and time spans to produce powerful pattern queries that are difficult to express with other formalisms. We characterize the range of queries PatternFinder supports as users vary the specificity at which events and time spans are defined. Pattern Finder{\textquoteright}s query capabilities together with coupled ball-and-chain and tabular visualizations enable users to effectively query, explore and analyze event patterns both within and across data entities (e.g. patient histories, terrorist groups, Web logs, etc.)},
	keywords = {ball-and-chain visualization, Chromium, Computer science, Data analysis, data visualisation, Data visualization, Database languages, event pattern discovery, Graphical user interfaces, History, Information Visualization, Medical treatment, multivariate temporal data, Pattern analysis, pattern recognition, PatternFinder integrated interface, Query processing, query visualization, result-set visualization, Spatial databases, tabular visualization, temporal pattern discovery, temporal pattern searching, Temporal query, user interface, User interfaces, visual databases, visual interface},
	isbn = {1-4244-0591-2},
	doi = {10.1109/VAST.2006.261421},
	author = {Fails,J. A and Karlson,A. and Shahamat,L. and Shneiderman, Ben}
}
@conference {17444,
	title = {Turning information visualization innovations into commercial products: lessons to guide the next success},
	booktitle = {IEEE Symposium on Information Visualization, 2005. INFOVIS 2005},
	year = {2005},
	month = {2005/10/23/25},
	pages = {241 - 244},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {As information visualization matures as an academic research field, commercial spinoffs are proliferating, but success stories are harder to find. This is the normal process of emergence for new technologies, but the panel organizers believe that there are certain strategies that facilitate success. To teach these lessons, we have invited several key figures who are seeking to commercialize information visualization tools. The panelists make short presentations, engage in a moderated discussion, and respond to audience questions.},
	keywords = {Books, commercial development, commercial product, Computer interfaces, Computer science, data visualisation, Data visualization, Educational institutions, exploratory data analysis, information visualization innovation, information visualization tool, innovation management, Laboratories, Management training, new technology emergence, Technological innovation, technology transfer, Turning, User interfaces},
	isbn = {0-7803-9464-X},
	doi = {10.1109/INFVIS.2005.1532153},
	author = {Shneiderman, Ben and Rao,R. and Andrews,K. and Ahlberg,C. and Brodbeck,D. and Jewitt,T. and Mackinlay,J.}
}
@conference {16462,
	title = {Exploiting multiple paths to express scientific queries},
	booktitle = {16th International Conference on Scientific and Statistical Database Management, 2004. Proceedings},
	year = {2004},
	month = {2004/06/21/23},
	pages = {357 - 360},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The purpose of this demonstration is to present the main features of the BioNavigation system. Scientific data collection needed in various stages of scientific discovery is typically performed manually. For each scientific object of interest (e.g., a gene, a sequence), scientists query a succession of Web resources following links between retrieved entries. Each of the steps provides part of the intended characterization of the scientific object. This process is sometimes partially supported by hard-coded scripts or complex queries that will be evaluated by a mediation-based data integration system or against a data warehouse. These approaches fail in guiding the scientists during the collection process. In contrast, the BioNavigation approach presented in the paper provides the scientists with information on the available alternative resources, their provenance, and the costs of data collection. The BioNavigation system enhances a mediation-based integration system and provides scientists with support for the following: to ask queries at a high conceptual level; to visualize the multiple alternative resources that may be exploited to execute their data collection queries; to choose the final execution path to evaluate their queries.},
	keywords = {access protocols, biology computing, BioNavigation system, complex queries, Costs, Data analysis, data handling, Data visualization, data warehouse, Data warehouses, Databases, diseases, distributed databases, hard-coded scripts, information resources, Information retrieval, mediation-based data integration system, multiple paths, query evaluation, Query processing, scientific data collection, scientific discovery, scientific information, scientific information systems, scientific object of interest, scientific queries, sequences, Web resources},
	isbn = {0-7695-2146-0},
	doi = {10.1109/SSDM.2004.1311231},
	author = {Lacroix,Z. and Moths,T. and Parekh,K. and Raschid, Louiqa and Vidal,M. -E}
}
@conference {17155,
	title = {Extending the utility of treemaps with flexible hierarchy},
	booktitle = {Eighth International Conference on Information Visualisation, 2004. IV 2004. Proceedings},
	year = {2004},
	month = {2004/07/14/16},
	pages = {335 - 344},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Treemaps are a visualization technique for presenting hierarchical information on two-dimensional displays. Prior implementations limit the visualization to pre-defined static hierarchies. Flexible hierarchy, a new capability of Treemap 4.0, enables users to define various hierarchies through dynamically selecting a series of data attributes so that they can discover patterns, clusters and outliers. This work describes the design and implementation issues of flexible hierarchy. It then reports on a usability study, which led to enhancements to the interface.},
	keywords = {2D displays, Computer displays, Computer science, data visualisation, Data visualization, Educational institutions, flexible hierarchy, graphical user interface, Graphical user interfaces, hierarchical information, Nominations and elections, Switches, Tree data structures, Tree graphs, Treemap 4.0, Two dimensional displays, usability, visualization technique},
	isbn = {0-7695-2177-0},
	doi = {10.1109/IV.2004.1320166},
	author = {Chintalapani,G. and Plaisant, Catherine and Shneiderman, Ben}
}
@conference {16944,
	title = {A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections},
	booktitle = {IEEE Symposium on Information Visualization, 2004. INFOVIS 2004},
	year = {2004},
	month = {2004///},
	pages = {65 - 72},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine each dimension first and then find relationships among dimensions, and (2) to try graphical displays first and then find numerical summaries (D.S. Moore, (1999). We implement these principles in a novel conceptual framework called the rank-by-feature framework. In the framework, users can choose a ranking criterion interesting to them and sort 1D or 2D axis-parallel projections according to the criterion. We introduce the rank-by-feature prism that is a color-coded lower-triangular matrix that guides users to desired features. Statistical graphs (histogram, boxplot, and scatterplot) and information visualization techniques (overview, coordination, and dynamic query) are combined to help users effectively traverse 1D and 2D axis-parallel projections, and finally to help them interactively find interesting features},
	keywords = {axis-parallel projections, boxplot, color-coded lower-triangular matrix, computational complexity, computational geometry, Computer displays, Computer science, Computer vision, Data analysis, data mining, data visualisation, Data visualization, Displays, dynamic query, Educational institutions, exploratory data analysis, feature detection, feature detection/selection, Feature extraction, feature selection, graph theory, graphical displays, histogram, Information Visualization, interactive systems, Laboratories, Multidimensional systems, Principal component analysis, rank-by-feature prism, scatterplot, statistical analysis, statistical graphics, statistical graphs, unsupervised multidimensional data exploration, very large databases},
	isbn = {0-7803-8779-3},
	doi = {10.1109/INFVIS.2004.3},
	author = {Seo,J. and Shneiderman, Ben}
}
@article {17259,
	title = {Interactively exploring hierarchical clustering results [gene identification]},
	journal = {Computer},
	volume = {35},
	year = {2002},
	month = {2002/07//},
	pages = {80 - 86},
	abstract = {To date, work in microarrays, sequenced genomes and bioinformatics has focused largely on algorithmic methods for processing and manipulating vast biological data sets. Future improvements will likely provide users with guidance in selecting the most appropriate algorithms and metrics for identifying meaningful clusters-interesting patterns in large data sets, such as groups of genes with similar profiles. Hierarchical clustering has been shown to be effective in microarray data analysis for identifying genes with similar profiles and thus possibly with similar functions. Users also need an efficient visualization tool, however, to facilitate pattern extraction from microarray data sets. The Hierarchical Clustering Explorer integrates four interactive features to provide information visualization techniques that allow users to control the processes and interact with the results. Thus, hybrid approaches that combine powerful algorithms with interactive visualization tools will join the strengths of fast processors with the detailed understanding of domain experts},
	keywords = {algorithmic methods, arrays, Bioinformatics, biological data sets, biology computing, Data analysis, data mining, data visualisation, Data visualization, DNA, Fluorescence, gene functions, gene identification, gene profiles, Genetics, Genomics, Hierarchical Clustering Explorer, hierarchical systems, interactive exploration, interactive information visualization tool, interactive systems, Large screen displays, meaningful cluster identification, metrics, microarray data analysis, pattern clustering, pattern extraction, Process control, Sensor arrays, sequenced genomes, Tiles},
	isbn = {0018-9162},
	doi = {10.1109/MC.2002.1016905},
	author = {Seo,Jinwook and Shneiderman, Ben}
}
@conference {17859,
	title = {Scheduling multiple data visualization query workloads on a shared memory machine},
	booktitle = {Parallel and Distributed Processing Symposium., Proceedings International, IPDPS 2002, Abstracts and CD-ROM},
	year = {2002},
	month = {2002///},
	pages = {11 - 18},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Query scheduling plays an important role when systems are faced with limited resources and high workloads. It becomes even more relevant for servers applying multiple query optimization techniques to batches of queries, in which portions of datasets as well as intermediate results are maintained in memory to speed up query evaluation. We present a dynamic query scheduling model based on a priority queue implementation using a directed graph and a strategy for ranking queries. We examine the relative performance of several ranking strategies on a shared-memory machine using two different versions of an application, called the Virtual Microscope, for browsing digitized microscopy images},
	keywords = {Atomic force microscopy, Biomedical informatics, Computer science, Data analysis, data visualisation, Data visualization, datasets, deductive databases, digitized microscopy image browsing, directed graph, directed graphs, dynamic query scheduling model, Educational institutions, high workloads, image database, limited resources, multiple data visualization query workloads, multiple query optimization, performance, priority queue, Processor scheduling, Query processing, query ranking, Relational databases, scheduling, shared memory machine, shared memory systems, Virtual Microscope, visual databases},
	isbn = {0-7695-1573-8},
	doi = {10.1109/IPDPS.2002.1015482},
	author = {Andrade,H. and Kurc, T. and Sussman, Alan and Saltz, J.}
}
@conference {17101,
	title = {Dynamic queries and brushing on choropleth maps},
	booktitle = {Fifth International Conference on Information Visualisation, 2001. Proceedings},
	year = {2001},
	month = {2001///},
	pages = {757 - 764},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Users who must combine demographic, economic or other data in a geographic context are often hampered by the integration of tabular and map representations. Static, paper-based solutions limit the amount of data that can be placed on a single map or table. By providing an effective user interface, we believe that researchers, journalists, teachers, and students can explore complex data sets more rapidly and effectively. This paper presents Dynamaps, a generalized map-based information visualization tool for dynamic queries and brushing on choropleth maps. Users can use color coding to show a variable on each geographic region, and then filter out areas that do not meet the desired criteria. In addition, a scatterplot view and a details-on-demand window support overviews and specific fact-finding},
	keywords = {brushing, business, cartography, choropleth maps, color coding, colour graphics, complex data sets, Computational Intelligence Society, Computer science, data visualisation, Data visualization, demographic data, Demography, Dynamaps, dynamic queries, economic data, Educational institutions, geographic information system, geographic information systems, Joining processes, map representations, map-based information visualization, Query processing, Scattering, scatterplot view, tabular representations, user interface, User interfaces, World Wide Web},
	isbn = {0-7695-1195-3},
	doi = {10.1109/IV.2001.942141},
	author = {Dang,G. and North,C. and Shneiderman, Ben}
}
@conference {16466,
	title = {Optimized seamless integration of biomolecular data},
	booktitle = {Proceedings of the IEEE 2nd International Symposium on Bioinformatics and Bioengineering Conference, 2001},
	year = {2001},
	month = {2001/11/04/6},
	pages = {23 - 32},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Today, scientific data is inevitably digitized, stored in a variety of heterogeneous formats, and is accessible over the Internet. Scientists need to access an integrated view of multiple remote or local heterogeneous data sources. They then integrate the results of complex queries and apply further analysis and visualization to support the task of scientific discovery. Building a digital library for scientific discovery requires accessing and manipulating data extracted from flat files or databases, documents retrieved from the Web, as well as data that is locally materialized in warehouses or is generated by software. We consider several tasks to provide optimized and seamless integration of biomolecular data. Challenges to be addressed include capturing and representing source capabilities; developing a methodology to acquire and represent metadata about source contents and access costs; and decision support to select sources and capabilities using cost based and semantic knowledge, and generating low cost query evaluation plans},
	keywords = {analysis, Bioinformatics, biology computing, cost based knowledge, Costs, Data analysis, data mining, data visualisation, Data visualization, Data warehouses, decision support, digital library, Educational institutions, information resources, Internet, low cost query evaluation plans, Mediation, meta data, metadata, molecular biophysics, multiple local heterogeneous data sources, multiple remote heterogeneous data sources, optimized seamless biomolecular data integration, scientific discovery, scientific information systems, semantic knowledge, software libraries, visual databases, Visualization},
	isbn = {0-7695-1423-5},
	doi = {10.1109/BIBE.2001.974408},
	author = {Eckman,B. A and Lacroix,Z. and Raschid, Louiqa}
}
@conference {17314,
	title = {Ordered treemap layouts},
	booktitle = {IEEE Symposium on Information Visualization, 2001. INFOVIS 2001},
	year = {2001},
	month = {2001///},
	pages = {73 - 78},
	publisher = {IEEE},
	organization = {IEEE},
	keywords = {Clustering algorithms, Computer science, Data visualization, Displays, Electronic switching systems, Filling, Monte Carlo methods, Read only memory, Testing},
	isbn = {0-7695-7342-5},
	doi = {10.1109/INFVIS.2001.963283},
	author = {Shneiderman, Ben and Wattenberg,M.}
}
@article {17842,
	title = {Visualization of large data sets with the Active Data Repository},
	journal = {IEEE Computer Graphics and Applications},
	volume = {21},
	year = {2001},
	month = {2001/08//Jul},
	pages = {24 - 33},
	abstract = {We implement ray-casting-based volume rendering and isosurface rendering methods using the Active Data Repository (ADR) for visualizing out-of-core data sets. We have developed the ADR object-oriented framework to provide support for applications that employ range queries with user-defined mapping and aggregation operations on large-scale multidimensional data. ADR targets distributed-memory parallel machines with one or more disks attached to each node. It is designed as a set of modular services implemented in C++, which can be customized for application-specific processing. The ADR runtime system supports common operations such as memory management, data retrieval, and scheduling of processing across a parallel machine},
	keywords = {active data repository, ADR runtime system, Algorithm design and analysis, application-specific processing, C++, data mining, data retrieval, data visualisation, Data visualization, distributed-memory parallel machines, Indexing, Information retrieval, isosurface rendering, Isosurfaces, large data sets, large-scale multidimensional data, Memory management, modular services, out-of-core data sets, parallel machine, Parallel machines, Partitioning algorithms, ray-casting-based volume rendering, Rendering (computer graphics), Runtime, software libraries, storage management},
	isbn = {0272-1716},
	doi = {10.1109/38.933521},
	author = {Kurc, T. and Catalyurek,U. and Chang,Chialin and Sussman, Alan and Saltz, J.}
}
@conference {17056,
	title = {Design and evaluation of incremental data structures and algorithms for dynamic query interfaces},
	booktitle = {IEEE Symposium on Information Visualization, 1997. Proceedings},
	year = {1997},
	month = {1997/10/21/21},
	pages = {81 - 86},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A dynamic query interface (DQI) is a database access mechanism that provides continuous real-time feedback to the user during query formulation. Previous work shows that DQIs are elegant and powerful interfaces to small databases. Unfortunately, when applied to large databases, previous DQI algorithms slow to a crawl. We present a new incremental approach to DQI algorithms and display updates that work well with large databases, both in theory and in practice.},
	keywords = {Algorithm design and analysis, Bars, Computer science, continuous real-time feedback, Data structures, data visualisation, Data visualization, database access mechanism, Displays, DQI algorithms, dynamic query interfaces, Feedback, Graphical user interfaces, Heuristic algorithms, incremental data structures, Information Visualization, large databases, Manipulator dynamics, NASA, query formulation, query languages, Query processing, real-time systems, small databases, User interfaces, very large databases, visual databases, visual languages},
	isbn = {0-8186-8189-6},
	doi = {10.1109/INFVIS.1997.636790},
	author = {Tanin,E. and Beigel,R. and Shneiderman, Ben}
}
@conference {17412,
	title = {The eyes have it: a task by data type taxonomy for information visualizations},
	booktitle = {, IEEE Symposium on Visual Languages, 1996. Proceedings},
	year = {1996},
	month = {1996/09/03/6},
	pages = {336 - 343},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A useful starting point for designing advanced graphical user interfaces is the visual information seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. The paper offers a task by data type taxonomy with seven data types (one, two, three dimensional data, temporal and multi dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts)},
	keywords = {advanced graphical user interface design, Art, data mining, data type taxonomy, data visualisation, Data visualization, Displays, Eyes, Graphical user interfaces, Information filtering, Information filters, information visualizations, multi dimensional data, Multimedia databases, network data, Taxonomy, visual databases, visual information seeking, visual programming},
	isbn = {0-8186-7508-X},
	doi = {10.1109/VL.1996.545307},
	author = {Shneiderman, Ben}
}
@conference {14760,
	title = {Dynamic program instrumentation for scalable performance tools},
	booktitle = {Scalable High-Performance Computing Conference, 1994., Proceedings of the},
	year = {1994},
	month = {1994/05//},
	pages = {841 - 850},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Presents a new technique called {\textquoteleft}dynamic instrumentation{\textquoteright} that provides efficient, scalable, yet detailed data collection for large-scale parallel applications. Our approach is unique because it defers inserting any instrumentation until the application is in execution. We can insert or change instrumentation at any time during execution by modifying the application{\textquoteright}s binary image. Only the instrumentation required for the currently selected analysis or visualization is inserted. As a result, our technique collects several orders of magnitude less data than traditional data collection approaches. We have implemented a prototype of our dynamic instrumentation on the CM-5, and present results for several real applications. In addition, we include recommendations to operating system designers, compiler writers, and computer architects about the features necessary to permit efficient monitoring of large-scale parallel systems},
	keywords = {Application software, binary image, compiler writing, Computer architecture, Computer displays, Computerized monitoring, Concurrent computing, data acquisition, data collection, data visualisation, Data visualization, dynamic program instrumentation, efficient monitoring, executing program, Instruments, large-scale parallel applications, Large-scale systems, operating system design, Operating systems, parallel programming, program analysis, program diagnostics, program visualization, Programming profession, Sampling methods, scalable performance tools, software tools},
	isbn = {0-8186-5680-8},
	doi = {10.1109/SHPCC.1994.296728},
	author = {Hollingsworth, Jeffrey K and Miller, B. P and Cargille, J.}
}
