@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 {16036,
	title = {LifeFlow: visualizing an overview of event sequences},
	booktitle = {Proceedings of the 2011 annual conference on Human factors in computing systems},
	series = {CHI {\textquoteright}11},
	year = {2011},
	month = {2011///},
	pages = {1747 - 1756},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Event sequence analysis is an important task in many domains: medical researchers may study the patterns of transfers within the hospital for quality control; transportation experts may study accident response logs to identify best practices. In many cases they deal with thousands of records. While previous research has focused on searching and browsing, overview tasks are often overlooked. We introduce a novel interactive visual overview of event sequences called \emph{LifeFlow}. LifeFlow is scalable, can summarize all possible sequences, and represents the temporal spacing of the events within sequences. Two case studies with healthcare and transportation domain experts are presented to illustrate the usefulness of LifeFlow. A user study with ten participants confirmed that after 15 minutes of training novice users were able to rapidly answer questions about the prevalence and temporal characteristics of sequences, find anomalies, and gain significant insight from the data.},
	keywords = {Information Visualization, overview visualization, temporal categorical data, timestamped event sequences},
	isbn = {978-1-4503-0228-9},
	doi = {10.1145/1978942.1979196},
	url = {http://doi.acm.org/10.1145/1978942.1979196},
	author = {Wongsuphasawat,Krist and Guerra G{\'o}mez,John Alexis and Plaisant, Catherine and Wang,Taowei David and Taieb-Maimon,Meirav and Shneiderman, Ben}
}
@conference {17280,
	title = {LifeFlow: visualizing an overview of event sequences (video preview)},
	booktitle = {Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems},
	series = {CHI EA {\textquoteright}11},
	year = {2011},
	month = {2011///},
	pages = {507 - 510},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Event sequence analysis is an important task in many domains: medical researchers may study the patterns of transfers within the hospital for quality control; transportation experts may study accident response logs to identify best practices. In many cases they deal with thousands of records. While previous research has focused on searching and browsing, overview tasks are often overlooked. We introduce a novel interactive visual overview of event sequences called LifeFlow. LifeFlow is scalable, can summarize all possible sequences, and represents the temporal spacing of the events within sequences. In this video, we show an example of patient transfer data and briefly demonstrate how to analyze them with LifeFlow. Please see [11] or visit http:www.cs.umd.eduhcillifeflow for more detail.},
	keywords = {emergency room, healthcare, Information Visualization, overview visualization, temporal categorical data, timestamped event sequences},
	isbn = {978-1-4503-0268-5},
	doi = {10.1145/1979742.1979557},
	url = {http://doi.acm.org/10.1145/1979742.1979557},
	author = {Wongsuphasawat,Krist and Guerra G{\'o}mez,John Alexis and Plaisant, Catherine and Wang,Taowei and Taieb-Maimon,Meirav and Shneiderman, Ben}
}
@conference {17306,
	title = {NetVisia: Heat Map \& Matrix Visualization of Dynamic Social Network Statistics \& Content},
	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 = {19 - 26},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Visualizations of static networks in the form of node-link diagrams have evolved rapidly, though researchers are still grappling with how best to show evolution of nodes over time in these diagrams. This paper introduces NetVisia, a social network visualization system designed to support users in exploring temporal evolution in networks by using heat maps to display node attribute changes over time. NetVisia{\textquoteright}s novel contributions to network visualizations are to (1) cluster nodes in the heat map by similar metric values instead of by topological similarity, and (2) align nodes in the heat map by events. We compare NetVisia to existing systems and describe a formative user evaluation of a NetVisia prototype with four participants that emphasized the need for tool tips and coordinated views. Despite the presence of some usability issues, in 30-40 minutes the user evaluation participants discovered new insights about the data set which had not been discovered using other systems. We discuss implemented improvements to NetVisia, and analyze a co-occurrence network of 228 business intelligence concepts and entities. This analysis confirms the utility of a clustered heat map to discover outlier nodes and time periods.},
	keywords = {business intelligence concept, business intelligence entity, competitive intelligence, data visualisation, dynamic networks, dynamic social network, heat map, Heating, Image color analysis, Information Visualization, Layout, matrix visualization, measurement, NetVisia system, network evolution, network visualization, node-link diagrams, outlier node, social network content, Social network services, social network statistics, social networking (online), social networks, static network visualization, time period, topological similarity, Training, usability, user evaluation, User interfaces},
	isbn = {978-1-4577-1931-8},
	doi = {10.1109/PASSAT/SocialCom.2011.216},
	author = {Gove,R. and Gramsky,N. and Kirby,R. and Sefer,E. and Sopan,A. and Dunne,C. and Shneiderman, Ben and Taieb-Maimon,M.}
}
@book {16975,
	title = {Analyzing Social Media Networks with NodeXL},
	year = {2010},
	month = {2010/08/27/},
	publisher = {Morgan Kaufmann},
	organization = {Morgan Kaufmann},
	abstract = {"Analyzing Social Media Networks with NodeXL provides a much needed resource for the social media research community, as it describes network theory, provides compelling examples using data sources like Twitter and Flickr, and highlights how to use a free sophisticated tool for analysis. This is the perfect book for anyone trying to analyze the behavior of online social networks and beyond." ---Adam Perer, Research Scientist, IBM Research  "This book provides a basic introduction to social network analysis, followed by practical instruction and examples on gathering data from online sources, importing into Excel, and then analyzing the data through Excel. The book will be important for promoting research in the area for those in information science, sociology, cultural studies, virtual community, and e-commerce."---Caroline Haythornthwaite, PhD, Professor, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign  Businesses, entrepreneurs, individuals, and government agencies alike are looking to social network analysis (SNA) tools for insight into trends, connections, and fluctuations in social media. Microsoft{\textquoteright}s NodeXL is a free, open-source SNA plug-in for use with Excel. It provides instant graphical representation of relationships of complex networked data. But it goes further than other SNA tools - NodeXL was developed by a multidisciplinary team of experts that bring together information studies, computer science, sociology, human-computer interaction, and over 20 years of visual analytic theory and information visualization into a simple tool anyone can use. This makes NodeXL of interest not only to end-users but also to researchers and students studying visual and network analytics and their application in the real world.},
	keywords = {Business \& Economics / Marketing / Research, Computers / Computer Science, Computers / Data Processing, Computers / Database Management / Data Mining, Computers / General, Computers / Information Theory, Computers / Interactive \& Multimedia, Computers / Social Aspects / General, Computers / Social Aspects / Human-Computer Interaction, Computers / User Interfaces, data mining, Data mining - Computer programs, Data mining/ Computer programs, Information Visualization, Information visualization - Computer programs, Information visualization/ Computer programs, NodeXL, online social networks, Social Science / General, Social Science / Reference, Social Science / Sociology / General, Technology \& Engineering / General, Technology \& Engineering / Social Aspects, Webometrics, Webometrics - Computer programs, Webometrics/ Computer programs},
	isbn = {9780123822291},
	author = {Hansen,Derek and Shneiderman, Ben and Smith,Marc A.}
}
@conference {13422,
	title = {An animated multivariate visualization for physiological and clinical data in the ICU},
	booktitle = {Proceedings of the 1st ACM International Health Informatics Symposium},
	series = {IHI {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {771 - 779},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Current visualizations of electronic medical data in the Intensive Care Unit (ICU) consist of stacked univariate plots of variables over time and a tabular display of the current numeric values for the corresponding variables and occasionally an alarm limit. The value of information is dependent upon knowledge of historic values to determine a change in state. With the ability to acquire more historic information, providers need more sophisticated visualization tools to assist them in analyzing the data in a multivariate fashion over time. We present a multivariate time series visualization that is interactive and animated, and has proven to be as effective as current methods in the ICU for predicting an episode of acute hypotension in terms of accuracy, confidence, and efficiency with only 30-60 minutes of training.},
	keywords = {computational physiology, Information Visualization, multivariate, time series},
	isbn = {978-1-4503-0030-8},
	doi = {10.1145/1882992.1883109},
	url = {http://doi.acm.org/10.1145/1882992.1883109},
	author = {Ord{\'o}{\~n}ez,Patricia and desJardins, Marie and Lombardi,Michael and Lehmann,Christoph U. and Fackler,Jim}
}
@conference {16055,
	title = {ManyNets: an interface for multiple network analysis and visualization},
	booktitle = {Proceedings of the 28th international conference on Human factors in computing systems},
	series = {CHI {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {213 - 222},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Traditional network analysis tools support analysts in studying a single network. ManyNets offers these analysts a powerful new approach that enables them to work on multiple networks simultaneously. Several thousand networks can be presented as rows in a tabular visualization, and then inspected, sorted and filtered according to their attributes. The networks to be displayed can be obtained by subdivision of larger networks. Examples of meaningful subdivisions used by analysts include ego networks, community extraction, and time-based slices. Cell visualizations and interactive column overviews allow analysts to assess the distribution of attributes within particular sets of networks. Details, such as traditional node-link diagrams, are available on demand. We describe a case study analyzing a social network geared towards film recommendations by means of decomposition. A small usability study provides feedback on the use of the interface on a set of tasks issued from the case study.},
	keywords = {exploratory analysis, graphical user interface, Information Visualization, interaction, network analysis, table interface},
	isbn = {978-1-60558-929-9},
	doi = {10.1145/1753326.1753358},
	url = {http://doi.acm.org/10.1145/1753326.1753358},
	author = {Freire,Manuel and Plaisant, Catherine and Shneiderman, Ben and Golbeck,Jen}
}
@conference {16051,
	title = {Visual information seeking in multiple electronic health records: design recommendations and a process model},
	booktitle = {Proceedings of the 1st ACM International Health Informatics Symposium},
	series = {IHI {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {46 - 55},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Current electronic health record (EHR) systems facilitate the storage, retrieval, persistence, and sharing of patient data. However, the way physicians interact with EHRs has not changed much. More specifically, support for temporal analysis of a large number of EHRs has been lacking. A number of information visualization techniques have been proposed to alleviate this problem. Unfortunately, due to their limited application to a single case study, the results are often difficult to generalize across medical scenarios. We present the usage data of Lifelines2,[22] our information visualization system, and user comments, both collected over eight different medical case studies. We generalize our experience into an information-seeking process model for multiple EHRs. Based on our analysis, we make recommendations to future information visualization designers for EHRs on design requirements and future research directions.},
	keywords = {design requriements, electronic health records, human-computer interaction (hci), Information Visualization},
	isbn = {978-1-4503-0030-8},
	doi = {10.1145/1882992.1883001},
	url = {http://doi.acm.org/10.1145/1882992.1883001},
	author = {Wang,Taowei David and Wongsuphasawat,Krist and Plaisant, Catherine 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 {16069,
	title = {Aligning temporal data by sentinel events: discovering patterns in electronic health records},
	booktitle = {Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems},
	series = {CHI {\textquoteright}08},
	year = {2008},
	month = {2008///},
	pages = {457 - 466},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Electronic Health Records (EHRs) and other temporal databases contain hidden patterns that reveal important cause-and-effect phenomena. Finding these patterns is a challenge when using traditional query languages and tabular displays. We present an interactive visual tool that complements query formulation by providing operations to align, rank and filter the results, and to visualize estimates of the intervals of validity of the data. Display of patient histories aligned on sentinel events (such as a first heart attack) enables users to spot precursor, co-occurring, and aftereffect events. A controlled study demonstrates the benefits of providing alignment (with a 61\% speed improvement for complex tasks). A qualitative study and interviews with medical professionals demonstrates that the interface can be learned quickly and seems to address their needs.},
	keywords = {electronic health record, Evaluation, Information Visualization, search, temporal data, Uncertainty},
	isbn = {978-1-60558-011-1},
	doi = {10.1145/1357054.1357129},
	url = {http://doi.acm.org/10.1145/1357054.1357129},
	author = {Wang,Taowei David and Plaisant, Catherine and Quinn,Alexander J. and Stanchak,Roman and Murphy,Shawn and Shneiderman, Ben}
}
@conference {16073,
	title = {BELIV{\textquoteright}08: Beyond time and errors: novel evaluation methods for information visualization},
	booktitle = {CHI {\textquoteright}08 extended abstracts on Human factors in computing systems},
	series = {CHI EA {\textquoteright}08},
	year = {2008},
	month = {2008///},
	pages = {3913 - 3916},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Information visualization systems allow users to produce insights, innovations and discoveries. Evaluating such tools is a challenging task and the goal of BELIV{\textquoteright}08 is to make a step ahead in the comprehension of such a complex activity. Current evaluation methods exhibit noticeable limitations and researchers in the area experiment some frustration with evaluation processes that are time consuming and too often leading to unsatisfactory results. The most used evaluation metrics such as task time completion and number of errors appear insufficient to quantify the quality of an information visualization system; thus the name of the workshop: "beyond time and errors".},
	keywords = {Evaluation, Information Visualization},
	isbn = {978-1-60558-012-8},
	doi = {10.1145/1358628.1358955},
	url = {http://doi.acm.org/10.1145/1358628.1358955},
	author = {Bertini,Enrico and Perer,Adam and Plaisant, Catherine and Santucci,Giuseppe}
}
@conference {17239,
	title = {Integrating statistics and visualization: case studies of gaining clarity during exploratory data analysis},
	booktitle = {Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems},
	series = {CHI {\textquoteright}08},
	year = {2008},
	month = {2008///},
	pages = {265 - 274},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Although both statistical methods and visualizations have been used by network analysts, exploratory data analysis remains a challenge. We propose that a tight integration of these technologies in an interactive exploratory tool could dramatically speed insight development. To test the power of this integrated approach, we created a novel social network analysis tool, SocialAction, and conducted four long-term case studies with domain experts, each working on unique data sets with unique problems. The structured replicated case studies show that the integrated approach in SocialAction led to significant discoveries by a political analyst, a bibliometrician, a healthcare consultant, and a counter-terrorism researcher. Our contributions demonstrate that the tight integration of statistics and visualizations improves exploratory data analysis, and that our evaluation methodology for long-term case studies captures the research strategies of data analysts.},
	keywords = {case studies, Evaluation, exploratory data analysis, Information Visualization, social networks, Statistics},
	isbn = {978-1-60558-011-1},
	doi = {10.1145/1357054.1357101},
	url = {http://doi.acm.org/10.1145/1357054.1357101},
	author = {Perer,Adam and Shneiderman, Ben}
}
@article {17250,
	title = {Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {14},
	year = {2008},
	month = {2008/10//Sept},
	pages = {999 - 1014},
	abstract = {Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity{\textquoteright}s relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users{\textquoteright} confidence and satisfaction.},
	keywords = {algorithms, Computer Graphics, D-Dupe, data visualisation, database management systems, Databases, Factual, graphical user interface, Graphical user interfaces, human-centered computing, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Information Visualization, interactive entity resolution, relational context visualization, Relational databases, relational entity resolution algorithm, User interfaces, user-centered design, User-Computer Interface, visual analytic tool},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2008.55},
	author = {Kang,Hyunmo and Getoor, Lise and Shneiderman, Ben and Bilgic,M. and Licamele,L.}
}
@conference {17392,
	title = {Systematic yet flexible discovery: guiding domain experts through exploratory data analysis},
	booktitle = {Proceedings of the 13th international conference on Intelligent user interfaces},
	series = {IUI {\textquoteright}08},
	year = {2008},
	month = {2008///},
	pages = {109 - 118},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {During exploratory data analysis, visualizations are often useful for making sense of complex data sets. However, as data sets increase in size and complexity, static information visualizations decrease in comprehensibility. Interactive techniques can yield valuable discoveries, but current data analysis tools typically support only opportunistic exploration that may be inefficient and incomplete. We present a refined architecture that uses systematic yet flexible (SYF) design goals to guide domain expert users through complex exploration of data over days, weeks and months. The SYF system aims to support exploratory data analysis with some of the simplicity of an e-commerce check-out while providing added flexibility to pursue insights. The SYF system provides an overview of the analysis process, suggests unexplored states, allows users to annotate useful states, supports collaboration, and enables reuse of successful strategies. The affordances of the SYF system are demonstrated by integrating it into a social network analysis tool employed by social scientists and intelligence analysts. The SYF system is a tool-independent component and can be incorporated into other data analysis tools.},
	keywords = {exploratory data analysis, guides, Information Visualization, social networks, systematic yet flexible, wizards},
	isbn = {978-1-59593-987-6},
	doi = {10.1145/1378773.1378788},
	url = {http://doi.acm.org/10.1145/1378773.1378788},
	author = {Perer,Adam and Shneiderman, Ben}
}
@article {16998,
	title = {Browsing large online data tables using generalized query previews},
	journal = {Information Systems},
	volume = {32},
	year = {2007},
	month = {2007/05//},
	pages = {402 - 423},
	abstract = {Companies, government agencies, and other organizations are making their data available to the world over the Internet. They often use large online relational tables for this purpose. Users query such tables with front-ends that typically use menus or form fillin interfaces, but these interfaces rarely give users information about the contents and distribution of the data. Such a situation leads users to waste time and network/server resources posing queries that have zero- or mega-hit results. Generalized query previews enable efficient browsing of large online data tables by supplying data distribution information to users. The data distribution information provides continuous feedback about the size of the result set as the query is being formed. Our paper presents a new user interface architecture and discusses three controlled experiments (with 12, 16, and 48 participants). Our prototype systems provide flexible user interfaces for research and testing of the ideas. The user studies show that for exploratory querying tasks, generalized query previews can speed user performance for certain user domains and can reduce network/server load.},
	keywords = {Information Visualization, Internet, Online querying, World-wide web},
	isbn = {0306-4379},
	doi = {10.1016/j.is.2005.12.006},
	url = {http://www.sciencedirect.com/science/article/pii/S0306437905001213},
	author = {Tanin,Egemen and Shneiderman, Ben and Xie,Hairuo}
}
@article {17072,
	title = {Designing Semantic Substrates for Visual Network Exploration},
	journal = {Information VisualizationInformation Visualization},
	volume = {6},
	year = {2007},
	month = {2007/12/21/},
	pages = {281 - 300},
	abstract = {A semantic substrate is a spatial template for a network, where nodes are grouped into regions and laid out within each region according to one or more node attributes. This paper shows how users can be given control in designing their own substrates and how this ability leads to a different approach to network data exploration. Users can create a semantic substrate, enter their data, get feedback from domain experts, edit the semantic substrate, and iteratively continue this procedure until the domain experts are satisfied with the insights they have gained. We illustrate this process in two case studies with domain experts working with legal precedents and food webs. Guidelines for designing substrates are provided, including how to locate, size, and align regions in a substrate, which attributes to choose for grouping nodes into regions, how to select placement methods and which attributes to set as parameters of the selected placement method. Throughout the paper, examples are illustrated with NVSS 2.0, the network visualization tool developed to explore the semantic substrate idea.},
	keywords = {data exploration and analysis, Graphical user interfaces, Information Visualization, network visualization, semantic substrate design},
	isbn = {1473-8716, 1473-8724},
	doi = {10.1057/palgrave.ivs.9500162},
	url = {http://ivi.sagepub.com/content/6/4/281},
	author = {Aris,Aleks and Shneiderman, Ben}
}
@conference {14477,
	title = {A dual-view approach to interactive network visualization},
	booktitle = {Proceedings of the sixteenth ACM conference on Conference on information and knowledge management},
	series = {CIKM {\textquoteright}07},
	year = {2007},
	month = {2007///},
	pages = {939 - 942},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Visualizing network data, from tree structures to arbitrarily connected graphs, is a difficult problem in information visualization. A large part of the problem is that in network data, users not only have to visualize the attributes specific to each data item, but also the links specifying how those items are connected to each other. Past approaches to resolving these difficulties focus on zooming, clustering, filtering and applying various methods of laying out nodes and edges. Such approaches, however, focus only on optimizing a network visualization in a single view, limiting the amount of information that can be shown and explored in parallel. Moreover, past approaches do not allow users to cross reference different subsets or aspects of large, complex networks. In this paper, we propose an approach to these limitations using multiple coordinated views of a given network. To illustrate our approach, we implement a tool called DualNet and evaluate the tool with a case study using an email communication network. We show how using multiple coordinated views improves navigation and provides insight into large networks with multiple node and link properties and types.},
	keywords = {coordinated views, Information Visualization, interactive graph visualization, social networks},
	isbn = {978-1-59593-803-9},
	doi = {10.1145/1321440.1321580},
	url = {http://doi.acm.org/10.1145/1321440.1321580},
	author = {Namata,Galileo Mark and Staats,Brian and Getoor, Lise and Shneiderman, Ben}
}
@article {17189,
	title = {Hierarchical Layouts for Photo Libraries},
	journal = {IEEE Multimedia},
	volume = {13},
	year = {2006},
	month = {2006/12//Oct},
	pages = {62 - 72},
	abstract = {We use an annotated digital photo collection to demonstrate a two-level auto-layout technique consisting of a central primary region with secondary regions surrounding it. Because the object sizes within regions can only be changed in discrete units, we refer to them as quantum content. Our real-time algorithms enable a compelling interactive display as users resize the canvas, or move and resize the primary region},
	keywords = {annotated digital photo collection, auto-layout technique, bi-level hierarchies, Computer science, data visualisation, digital libraries, document image processing, Information Visualization, interactive algorithms, interactive displays, Libraries, Lifting equipment, Organization Charts, photo collections, photo layouts, photo library, Photography, quantum content, Silver, Springs, User interfaces, Web pages},
	isbn = {1070-986X},
	doi = {10.1109/MMUL.2006.83},
	author = {Kustanowitz,J. 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}
}
@article {17271,
	title = {Knowledge Discovery in High Dimensional Data: Case Studies and a User Survey for an Information Visualization Tool},
	journal = {Institute for Systems Research Technical Reports},
	year = {2005},
	month = {2005///},
	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 3-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 email 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 = {Information Visualization},
	url = {http://drum.lib.umd.edu/handle/1903/6566},
	author = {Seo,Jinwook and Shneiderman, Ben}
}
@article {16942,
	title = {A Rank-by-Feature Framework for Interactive Exploration of Multidimensional Data},
	journal = {Information VisualizationInformation Visualization},
	volume = {4},
	year = {2005},
	month = {2005/06/20/},
	pages = {96 - 113},
	abstract = {Interactive exploration of multidimensional data sets is challenging because: (1) it is difficult to comprehend patterns in more than three dimensions, and (2) current systems often are a patchwork of graphical and statistical methods leaving many researchers uncertain about how to explore their data in an orderly manner. We offer a set of principles and a novel rank-by-feature framework that could enable users to better understand distributions in one (1D) or two dimensions (2D), and then discover relationships, clusters, gaps, outliers, and other features. Users of our framework can view graphical presentations (histograms, boxplots, and scatterplots), and then choose a feature detection criterion to rank 1D or 2D axis-parallel projections. By combining information visualization techniques (overview, coordination, and dynamic query) with summaries and statistical methods users can systematically examine the most important 1D and 2D axis-parallel projections. We summarize our Graphics, Ranking, and Interaction for Discovery (GRID) principles as: (1) study 1D, study 2D, then find features (2) ranking guides insight, statistics confirm. We implemented the rank-by-feature framework in the Hierarchical Clustering Explorer, but the same data exploration principles could enable users to organize their discovery process so as to produce more thorough analyses and extract deeper insights in any multidimensional data application, such as spreadsheets, statistical packages, or information visualization tools.},
	keywords = {dynamic query, exploratory data analysis, feature detection/selection, graphical displays, Information Visualization, Rank-by-feature framework},
	isbn = {1473-8716, 1473-8724},
	doi = {10.1057/palgrave.ivs.9500091},
	url = {http://ivi.sagepub.com/content/4/2/96},
	author = {Seo,Jinwook and Shneiderman, Ben}
}
@conference {17159,
	title = {Facilitating understanding of information visualizations: emerging principles and examples},
	booktitle = {Eighth International Conference on Information Visualisation, 2004. IV 2004. Proceedings},
	year = {2004},
	month = {2004/07/14/16},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Summary form only given. The enthusiasm for information visualization has generated a wide variety of interesting tools for multi-dimensional, hierarchical, and other kinds of visualizations. However, some designs are readily accepted as understandable and useful, while others are perceived as confusing and useless. Controlled studies have begun to sort of some of the issues, but the insights of designers and usability tests are contributing interesting cognitive hypotheses for researchers and practical guidelines for developers. This paper offers examples of what works and what doesn{\textquoteright}t with a preliminary set of principles that might have wide applicability.},
	keywords = {Computer science, data visualisation, Educational institutions, Guidelines, hierarchical visualization, HUMANS, Information Visualization, Laboratories, multidimensional visualization, Portable computers, Testing, usability, User interfaces, Visualization},
	isbn = {0-7695-2177-0},
	doi = {10.1109/IV.2004.1320117},
	author = {Shneiderman, Ben}
}
@conference {16121,
	title = {Les le{\c c}ons tir{\'e}es des deux comp{\'e}titions de visualisation d{\textquoteright}information},
	booktitle = {Proceedings of the 16th conference on Association Francophone d{\textquoteright}Interaction Homme-Machine},
	series = {IHM 2004},
	year = {2004},
	month = {2004///},
	pages = {7 - 12},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Information visualization needs benchmarks to carry on. A benchmark is aimed at comparing information visualization techniques or systems. A benchmark is made of a dataset, a list of tasks mostly based on finding facts about the dataset, and a list of interesting or important findings about the datasets (the nuggets to find). For the second year, we are organizing the InfoVis Contest aimed at collecting results for benchmarks. We describe here the main lessons we learned.},
	keywords = {benchmark, contest, Evaluation, Information Visualization},
	isbn = {1-58113-926-8},
	doi = {10.1145/1148613.1148616},
	url = {http://doi.acm.org/10.1145/1148613.1148616},
	author = {Fekete,Jean-Daniel and Plaisant, Catherine}
}
@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}
}
@book {17406,
	title = {The craft of information visualization: readings and reflections},
	year = {2003},
	month = {2003///},
	publisher = {Morgan Kaufmann},
	organization = {Morgan Kaufmann},
	abstract = {Since the beginning of the computer age, researchers from many disciplines have sought to facilitate people{\textquoteright}s use of computers and to provide ways for scientists to make sense of the immense quantities of data coming out of them. One gainful result of these efforts has been the field of information visualization, whose technology is increasingly applied in scientific research, digital libraries, data mining, financial data analysis, market studies, manufacturing production control, and data discovery.This book collects 38 of the key papers on information visualization from a leading and prominent research lab, the University of Maryland{\textquoteright}s Human-Computer Interaction Lab (HCIL). Celebrating HCIL{\textquoteright}s 20th anniversary, this book presents a coherent body of work from a respected community that has had many success stories with its research and commercial spin-offs. Each chapter contains an introduction specifically written for this volume by two leading HCI researchers, to describe the connections among those papers and reveal HCIL{\textquoteright}s individual approach to developing innovations.*Presents key ideas, novel interfaces, and major applications of information visualization tools, embedded in inspirational prototypes.*Techniques can be widely applied in scientific research, digital libraries, data mining, financial data analysis, business market studies, manufacturing production control, drug discovery, and genomic studies.*Provides an "insider" view to the scientific process and evolution of innovation, as told by the researchers themselves.*This work comes from the prominent and high profile University of Maryland{\textquoteright}s Human Computer Interaction Lab},
	keywords = {Computers / Computer Graphics, Computers / Computer Science, Computers / Digital Media / Desktop Publishing, Computers / General, Computers / Social Aspects / Human-Computer Interaction, Computers / User Interfaces, Human-computer interaction, Information Visualization, Science / General, User-centered system design},
	isbn = {9781558609150},
	author = {Bederson, Benjamin B. and Shneiderman, Ben}
}
@article {17508,
	title = {Why not make interfaces better than 3D reality?},
	journal = {IEEE Computer Graphics and Applications},
	volume = {23},
	year = {2003},
	month = {2003/12//Nov},
	pages = {12 - 15},
	abstract = {Many constrained interfaces are designed to be simpler than the real world by restricting movement, limiting interface actions, and keeping interface objects in a plane. However, the strong utility of pure 3D interfaces for medical, architectural, product design, and scientific visualization means that interface design for pure 3D remains an important challenge. An intriguing possibility is that enhanced 3D interfaces might offer simpler navigation, more compelling functionality, safer movements, and less occlusion, than 3D reality, especially for information exploration and visualization tasks. Such features can enable superhuman capabilities such as faster-than-light teleportation, flying through objects, and X-ray vision. Enhanced 3D interfaces might have supernatural tools such as magic wands for instantly shrinking, enlarging, duplicating, or sending objects and enchanted environments that provide error prevention, history keeping, and programming-by-demonstration. Playful game designers and creative application developers have already pushed the technology further than those who seek merely to mimic reality. Advanced designs are marked by their support of rapid situation awareness through effective overviews, reduced numbers of actions to accomplish tasks; and prompt, meaningful feedback for user actions. This article reviews these clever enhanced 3D-design features and encourages approaches that facilitate user tasks rather than mimic reality.},
	keywords = {3D interfaces, 3D reality, Atmosphere, Avatars, Cities and towns, Collaboration, Computer displays, Computer Graphics, error prevention, history keeping, information exploration, Information Visualization, movements, Navigation, occlusion, overviews, programming-by-demonstration, rapid situation awareness, Testing, usability, user action feedback, User interfaces, Virtual reality, Visualization},
	isbn = {0272-1716},
	doi = {10.1109/MCG.2003.1242376},
	author = {Shneiderman, Ben}
}
@conference {16921,
	title = {A dynamic query interface for finding patterns in time series data},
	booktitle = {CHI {\textquoteright}02 extended abstracts on Human factors in computing systems},
	series = {CHI EA {\textquoteright}02},
	year = {2002},
	month = {2002///},
	pages = {522 - 523},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Identification of patterns in time series data sets is a task that arises in a wide variety of application domains. This demonstration presents the timebox model of rectangular regions that specify constraints for dynamic queries over time series data sets, and the TimeSearcher application, which uses timeboxes as the basis of an interactive query tool.},
	keywords = {dynamic queries, Information Visualization, time series},
	isbn = {1-58113-454-1},
	doi = {10.1145/506443.506460},
	url = {http://doi.acm.org/10.1145/506443.506460},
	author = {Hochheiser,Harry and Shneiderman, Ben}
}
@article {17312,
	title = {Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies},
	journal = {ACM Transactions on Graphics (TOG)},
	volume = {21},
	year = {2002},
	month = {2002/10//},
	pages = {833 - 854},
	abstract = {Treemaps, a space-filling method for visualizing large hierarchical data sets, are receiving increasing attention. Several algorithms have been previously proposed to create more useful displays by controlling the aspect ratios of the rectangles that make up a treemap. While these algorithms do improve visibility of small items in a single layout, they introduce instability over time in the display of dynamically changing data, fail to preserve order of the underlying data, and create layouts that are difficult to visually search. In addition, continuous treemap algorithms are not suitable for displaying fixed-sized objects within them, such as images.This paper introduces a new "strip" treemap algorithm which addresses these shortcomings, and analyzes other "pivot" algorithms we recently developed showing the trade-offs between them. These ordered treemap algorithms ensure that items near each other in the given order will be near each other in the treemap layout. Using experimental evidence from Monte Carlo trials and from actual stock market data, we show that, compared to other layout algorithms, ordered treemaps are more stable, while maintaining relatively favorable aspect ratios of the constituent rectangles. A user study with 20 participants clarifies the human performance benefits of the new algorithms. Finally, we present quantum treemap algorithms, which modify the layout of the continuous treemap algorithms to generate rectangles that are integral multiples of an input object size. The quantum treemap algorithm has been applied to PhotoMesa, an application that supports browsing of large numbers of images.},
	keywords = {hierarchies, Human-computer interaction, image browsers, Information Visualization, jazz, ordered treemaps, treemaps, TREES, zoomable user interfaces (ZUIs).},
	isbn = {0730-0301},
	doi = {10.1145/571647.571649},
	url = {http://doi.acm.org/10.1145/571647.571649},
	author = {Bederson, Benjamin B. and Shneiderman, Ben and Wattenberg,Martin}
}
@conference {17025,
	title = {Component-based, user-constructed, multiple-view visualization},
	booktitle = {CHI {\textquoteright}01 extended abstracts on Human factors in computing systems},
	series = {CHI EA {\textquoteright}01},
	year = {2001},
	month = {2001///},
	pages = {201 - 202},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {A major hindrance to the usage of information visualization in common tasks is that typically a new visualization interface must be custom programmed to suit each task. This video demonstrates a system and user interface that attempts to solve this problem by enabling end users to construct their own multiple-view visualization interfaces that are appropriate for their tasks and data. Users accomplish this by snapping together component visualizations and specifying tight couplings between them. Then they can use their newly constructed visualization interfaces to perform their tasks.},
	keywords = {Information Visualization, multiple views, software components, Specification, tight coupling, user interface},
	isbn = {1-58113-340-5},
	doi = {10.1145/634067.634188},
	url = {http://doi.acm.org/10.1145/634067.634188},
	author = {North,Chris and Shneiderman, Ben}
}
@article {17036,
	title = {Creating creativity: user interfaces for supporting innovation},
	journal = {ACM Trans. Comput.-Hum. Interact.},
	volume = {7},
	year = {2000},
	month = {2000/03//},
	pages = {114 - 138},
	abstract = {A challenge for human-computer interaction researchers and user interf ace designers is to construct information technologies that support creativity. This ambitious goal can be attained by building on an adequate understanding of creative processes. This article offers a four-phase framework for creativity that might assist designers in providing effective tools for users: (1)Collect: learn from provious works stored in libraries, the Web, etc.; (2) Relate: consult with peers and mentors at early, middle, and late stages, (3)Create: explore, compose, evaluate possible solutions; and (4) Donate: disseminate the results and contribute to the libraries. Within this integrated framework, this article proposes eight  activities that require human-computer interaction research and advanced user interface design. A scenario about an architect illustrates the process of creative work within such an environment.},
	keywords = {creativity support tools, direct manipulation, Graphical user interfaces, Human-computer interaction, Information Visualization},
	isbn = {1073-0516},
	doi = {10.1145/344949.345077},
	url = {http://doi.acm.org/10.1145/344949.345077},
	author = {Shneiderman, Ben}
}
@conference {17361,
	title = {Snap-together visualization: a user interface for coordinating visualizations via relational schemata},
	booktitle = {Proceedings of the working conference on Advanced visual interfaces},
	series = {AVI {\textquoteright}00},
	year = {2000},
	month = {2000///},
	pages = {128 - 135},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Multiple coordinated visualizations enable users to rapidly explore complex information. However, users often need unforeseen combinations of coordinated visualizations that are appropriate for their data. Snap-Together Visualization enables data users to rapidly and dynamically mix and match visualizations and coordinations to construct custom exploration interfaces without programming. Snap{\textquoteright}s conceptual model is based on the relational database model. Users load relations into visualizations then coordinate them based on the relational joins between them. Users can create different types of coordinations such as: brushing, drill down, overview and detail view, and synchronized scrolling. Visualization developers can make their independent visualizations snap-able with a simple API.Evaluation of Snap revealed benefits, cognitive issues, and usability concerns. Data savvy users were very capable and thrilled to rapidly construct powerful coordinated visualizations. A snapped overview and detail-view coordination improved user performance by 30-80\%, depending on task.
},
	keywords = {Coordination, Information Visualization, multiple views, relational database, tight coupling, user interface, user study},
	isbn = {1-58113-252-2},
	doi = {10.1145/345513.345282},
	url = {http://doi.acm.org/10.1145/345513.345282},
	author = {North,Chris and Shneiderman, Ben}
}
@conference {17501,
	title = {Visualizing digital library search results with categorical and hierarchical axes},
	booktitle = {Proceedings of the fifth ACM conference on Digital libraries},
	series = {DL {\textquoteright}00},
	year = {2000},
	month = {2000///},
	pages = {57 - 66},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Digital library search results are usually shown as a textual list,    with 10-20 items per page. Viewing several thousand search results at once on a two-dimensional display with continuous variables is a promising       alternative. Since these displays can overwhelm some users, we created a    simplified two-dimensional display that uses categorical and hierarchical   axes, called hieraxes. Users appreciate the meaningful and limited number   of terms on each hieraxis. At each grid point of the display we show a      cluster of color-coded dots or a bar chart. Users see the entire result set and can then click on labels to move down a level in the hierarchy.         Handling broad hierarchies and arranging for imposed hierarchies led to     additional design innovations. We applied hieraxes to a digital video   library of science topics used by middle school teachers, a legal   information system, and a technical library using the ACM Computing Classification System. Feedback from usability testing with 32 subjects revealed strengths and weaknesses.},
	keywords = {categorical axes, digital libraries, Graphical user interfaces, hierarchy, hieraxes, Information Visualization},
	isbn = {1-58113-231-X},
	doi = {10.1145/336597.336637},
	url = {http://doi.acm.org/10.1145/336597.336637},
	author = {Shneiderman, Ben and Feldman,David and Rose,Anne and Grau,Xavier Ferr{\'e}}
}
@book {17339,
	title = {Readings in Information Visualization: Using Vision to Think},
	year = {1999},
	month = {1999/01/25/},
	publisher = {Morgan Kaufmann},
	organization = {Morgan Kaufmann},
	abstract = {This groundbreaking book defines the emerging field of information visualization and offers the first-ever collection of the classic papers of the discipline, with introductions and analytical discussions of each topic and paper. The authors{\textquoteright} intention is to present papers that focus on the use of visualization to discover relationships, using interactive graphics to amplify thought. This book is intended for research professionals in academia and industry; new graduate students and professors who want to begin work in this burgeoning field; professionals involved in financial data analysis, statistics, and information design; scientific data managers; and professionals involved in medical, bioinformatics, and other areas.* Full-color reproduction throughout* Author power team - an exciting and timely collaboration between the field{\textquoteright}s pioneering, most-respected names* The only book on Information Visualization with the depth necessary for use as a text or as a reference for the information professional* Text includes the classic source papers as well as a collection of cutting edge work},
	keywords = {Computer Graphics, Computers / Computer Engineering, Computers / Computer Graphics, Computers / Computer Graphics / General, Computers / Computer Science, Computers / General, Computers / Information Technology, Computers / Information Theory, Computers / Intelligence (AI) \& Semantics, Computers / Social Aspects / Human-Computer Interaction, IMAGE PROCESSING, Information display systems, Information Visualization, Psychology / General, Visualization},
	isbn = {9781558605336},
	author = {Card,Stuart K. and Mackinlay,Jock D. and Shneiderman, Ben}
}
@article {17479,
	title = {Using Interactive Visualizations of WWW Log Data to Characterize Access Patterns and Inform Site Design},
	year = {1999},
	month = {1999///},
	abstract = {HTTP server log files provide Web site operators with substantial detail regarding the visitors to their sites. Interest ininterpreting this data has spawned an active market for software packages that summarize and analyze this data, providing histograms, pie graphs, and other charts summarizing usage patterns. While useful, these summaries obscureuseful information and restrict users to passive interpretation of static displays. Interactive visualizations can be used to provide users with greater abilities to interpret and explore web log data. Bycombining two-dimensional displays of thousands of individual access requests, color and size coding for additional attributes, and facilities for zooming and filtering, these visualizations provide capabilities for examining data that exceed those of traditional web log analysis tools. <p>We introduce a series of interactive visualizations that can be used to explore server data across various dimensions. Sample visualizations of server data from two web sites are presented. Coordinated, snap-together visualizations (STVs) of log data are introduced as a means of gaining additional expressive power. Possible uses of these visualizations are discussed, and difficulties of data collection, presentation, and interpretation are explored.},
	keywords = {Information Visualization, log file analysis, World Wide Web},
	url = {http://drum.lib.umd.edu/handle/1903/6034},
	author = {Hochheiser,Harry and Shneiderman, Ben}
}
@conference {16191,
	title = {Data object and label placement for information abundant visualizations},
	booktitle = {Proceedings of the 1998 workshop on New paradigms in information visualization and manipulation},
	series = {NPIV {\textquoteright}98},
	year = {1998},
	month = {1998///},
	pages = {41 - 48},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {control panel, data object placement, Information Visualization, label placement, metrics, timelines, visual feedback},
	isbn = {1-58113-179-8},
	doi = {10.1145/324332.324341},
	url = {http://doi.acm.org/10.1145/324332.324341},
	author = {Li,Jia and Plaisant, Catherine and Shneiderman, Ben}
}
@article {16184,
	title = {An information architecture to support the visualization of personal histories},
	journal = {Information Processing \& Management},
	volume = {34},
	year = {1998},
	month = {1998/09//},
	pages = {581 - 597},
	abstract = {This paper proposes an information architecture for personal history data and describes how the data model can be extended to a runtime model for a compact visualization using graphical timelines. Our information architecture was developed for juvenile justice and medical patient records, but is usable in other application domains such as personal resumes, financial histories, or customer support. Our model groups personal history events into aggregates that are contained in facets (e.g., doctor visits, hospitalizations, or lab tests). Crosslinks enable representation of arbitrary relationships across events and aggregates. Data attributes, such as severity, can be mapped by data administrators to visual attributes such as color and line thickness. End-users have powerful controls over the display contents, and they can modify the mapping to fit their tasks.},
	keywords = {Graphical user interfaces, Information Visualization, LifeLines, medical patient record, personal histories, temporal data, timelines},
	isbn = {0306-4573},
	doi = {10.1016/S0306-4573(98)00024-7},
	url = {http://www.sciencedirect.com/science/article/pii/S0306457398000247},
	author = {Plaisant, Catherine and Shneiderman, Ben and Mushlin,Rich}
}
@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}
}
@article {17231,
	title = {Incremental data structures and algorithms for dynamic query interfaces},
	journal = {ACM SIGMOD Record},
	volume = {25},
	year = {1996},
	month = {1996/12//},
	pages = {21 - 24},
	abstract = {Dynamic query interfaces (DQIs) form a recently developed method of database access that provides continuous realtime feedback to the user during the query formulation process. 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 approach to DQI algorithms that works well with large databases.},
	keywords = {algorithm, data structure, database, direct manipulation, dynamic query, Information Visualization, user interface},
	isbn = {0163-5808},
	doi = {10.1145/245882.245891},
	url = {http://doi.acm.org/10.1145/245882.245891},
	author = {Tanin,Egemen and Beigel,Richard and Shneiderman, Ben}
}
