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NetLens: Iterative Exploration of Content-Actor Network Data

Information Visualization 2007年第1期6卷 18 - 31页

作者： Hyunmo Kang Catherine Plaisant Bongshin Lee Benjamin B. Bederson Human-Computer Interaction Laboratory University of Maryland Institute for Advanced Computer Studies College Park MD 20742 U.S.A. Department of Computer Science University of Maryland College Park MD 20742 U.S.A.Microsoft Research One Microsoft Way Redmond WA 98052 U.S.A. Human-Computer Interaction Laboratory University of Maryland Institute for Advanced Computer Studies College Park MD 20742 U.S.A.Department of Computer Science University of Maryland College Park MD 20742 U.S.A.

Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. This paper offers an abstract Content-Actor network data model, a classification of tasks, and a tool to support them. The NetLens interface was designed around the abstract Content-Actor network data model to allow users to pose a series of elementary queries and iteratively refine visual overviews and sorted lists. This enables the support of complex queries that are traditionally hard to specify. NetLens is general and scalable in that it applies to any data set that can be represented with our abstract data model. This paper describes the use of NetLens with a subset of the ACM Digital Library consisting of about 4000 papers from the CHI conference written by about 6000 authors, and reports on a usability study with nine participants.

关键词： content-actor network data iterative query refinement incremental data exploration user interfaces digital library

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video Technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

No abstract available

关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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International Conference on Information Visualisation (IV)

作者： P. Buono C. Plaisant A. Simeone A. Aris B. Shneiderman G. Shmueli W. Jank Dipartimento di Informatica Università di Bari Bari Italy Human-Computer Interaction Laboratory University of Maryland USA Computer Science Department University of Maryland USA Robert H. Smith School of Business University of Maryland USA

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.

关键词： Rivers Pattern matching Economic forecasting Predictive models Data visualization Smoothing methods Testing Weather forecasting Laboratories Technological innovation

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Young researchers' views on the current and future state of HRI

Young researchers' views on the current and future state of ...

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ACM/IEEE International Conference on human-Robot interaction (HRI)

作者： Kevin Gold Ian Fasel Nathan G. Freier Cristen Torrey Department of Computer Science Yale University New Heaven CT USA Machine Perception Laboratory Institute for Neural Computation University of California San Diego La Jolla CA USA The Information School University of Washington Seattle WA USA Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9781595936172

This paper presents the results of a panel discussion titled “The Future of HRI,” held during an NSF workshop for graduate students on human-robot interaction in August 2006. The panel divided the workshop into groups tasked with inventing models of the field, and then asked these groups their opinions on the future of the field. In general, the workshop participants shared the belief that HRI can and should be seen as a single scientific discipline, despite the fact that it encompasses a variety of beliefs, methods, and philosophies drawn from several “core” disciplines in traditional areas of study. HRI researchers share many interrelated goals, participants felt, and enhancing the lines of communication between different areas would help speed up progress in the field. Common concerns included the unavailability of common robust platforms, the emphasis on human perception over robot perception, and the paucity of longitudinal real-world studies. The authors point to the current lack of consensus on research paradigms and platforms to argue that the field is not yet in the phase that philosopher Thomas Kuhn would call “normal science,” but believe the field shows signs of approaching that phase.

关键词： Robots humans human computer interaction Abstracts Argon

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Search result visualization panel

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Proceedings of the Association for Information science and Technology 2007年第1期43卷 1-2页

作者： Chaomei Chen Brad Eden Sherry Koshman Linn Marks Collins Ray Uzwyshyn College of Information Science and Technology Drexel University Web and Digitization Services University of Nevada Las Vegas Libraries School of Information Sciences University of Pittsburgh 135 N. Bellefield Ave. Pittsburgh PA 15260 e-Science and Human-Computer Interaction Research Library Los Alamos National Laboratory MS P362 Los Alamos NM 87544-7113 (Moderator) University of Miami Digital Library Initiatives University of Miami P.O.Box 248214 Coral Gables FL 33124-0320

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Strategies for evaluating information visualization tools: Multi-dimensional in-depth long-term case studies

Strategies for evaluating information visualization tools: M...

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BELIV'06: BEyond time and errors - novel EvaLuation methods for Information Visualization. A workshop of the AVI 2006 International Working Conference

作者： Shneiderman, Ben Plaisant, Catherine Human-Computer Interaction Laboratory Institute for Advanced Computer Studies Computer Science Department University of Maryland

ISBN: (纸本)1595935622

After an historical review of evaluation methods, we describe an emerging research method called Multi-dimensional In-depth Long-term Case studies (MILCs) which seems well adapted to study the creative activities that users of information visualization systems engage in. We propose that the efficacy of tools can be assessed by documenting 1) usage (observations, interviews, surveys, logging etc.) and 2) expert users' success in achieving their professional goals. We summarize lessons from related ethnography methods used in HCI and provide guidelines for conducting MILCs for information visualization. We suggest ways to refine the methods for MILCs in modest sized projects and then envision ambitious projects with 3-10 researchers working over 1-3 years to understand individual and organizational use of information visualization by domain experts working at the frontiers of knowledge in their fields. Copyright 2006 ACM.

关键词： Information analysis

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Visualizing graphs as trees: Plant a seed and watch it grow

Visualizing graphs as trees: Plant a seed and watch it grow

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13th International Symposium on Graph Drawing, GD 2005

作者： Lee, Bongshin Parr, Cynthia Sims Plaisant, Catherine Bederson, Benjamin B. Human-Computer Interaction Laboratory Computer Science Department University of Maryland College Park MD 20742 United States

ISBN: (纸本)3540314253

TreePlus is a graph browsing technique based on a tree-style layout. It shows the missing graph structure using interaction techniques and enables users to start with a specific node and incrementally explore the local structure of graphs. We believe that it supports particularly well tasks that require rapid reading of labels. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Graph theory

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NetLens: Iterative Exploration of Content-Actor Network Data

NetLens: Iterative Exploration of Content-Actor Network Data

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IEEE Symposium on Visual Analytics science and Technology (VAST)

作者： Hyunmo Kang Catherine Plaisant Bongshin Lee Benjamin B. Bederson Human-Computer Interaction Laboratory Institute for Advanced Computer Studies University of Maryland USA Computer Science Department Human-Computer Interaction (HCI) Laboratory Canada

Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. This paper offers an abstract content-actor network data model, a classification of tasks, and a tool to support them. The NetLens interface was designed around the abstract content-actor network data model to allow users to pose a series of elementary queries and iteratively refine visual overviews and sorted lists. This enables the support of complex queries that are traditionally hard to specify. NetLens is general and scalable in that it applies to any dataset that can be represented with our abstract data model. This paper describes NetLens applying a subset of the ACM Digital Library consisting of about 4,000 papers from the CM I conference written by about 6,000 authors. In addition, we are now working on a collection of half a million emails, and a dataset of legal cases

关键词： Data visualization Software libraries Data models Displays User interfaces Law Legal factors Graphical user interfaces Visual analytics Laboratories

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Categorizing web search results into meaningful and stable categories using fast-feature techniques

Categorizing web search results into meaningful and stable c...

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IEEE/ACM Joint Conference on Digital Libraries(JCDL)

作者： Jack Kustanowitz Ben Shneiderman Bill Kules Human-Computer Interaction Laboratory and Department of Computer Science University of Maryland College Park MD USA

When search results against digital libraries and Web resources have limited metadata, augmenting them with meaningful and stable category information can enable better overviews and support user exploration. This paper proposes six "fast-feature" techniques that use only features available in the search result list, such as title, snippet, and URL, to categorize results into meaningful categories. They use credible knowledge resources, including a US government organizational hierarchy, a thematic hierarchy from the open directory project (ODP) Web directory, and personal browse histories, to add valuable metadata to search results. In three tests the percent of results categorized for five representative queries was high enough to suggest practical benefits: general Web search (76-90%), government Web search (39-100%), and the Bureau of Labor Statistics Website (48-94%). An additional test submitted 250 TREC queries to a search engine and successfully categorized 66% of the top 100 using the ODP and 61% of the top 350. Fast-feature techniques have been implemented in a prototype search engine. We propose research directions to improve categorization rates and make suggestions about how Web site designers could re-organize their sites to support fast categorization of search results

关键词： Web search Testing Search engines Software libraries Uniform resource locators US Government History Statistical analysis Prototypes Web page design

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Exploring content-actor paired network data using iterative query refinement with NetLens

Exploring content-actor paired network data using iterative ...

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IEEE/ACM Joint Conference on Digital Libraries(JCDL)

作者： Benjamin B. Bederson Bongshin Lee Catherine Plaisant Hyunmo Kang Human-Computer Interaction Laboratory Institute for Advanced Computer Studies University of Maryland College Park MD USA Human-Computer Interaction Laboratory Computer Science Department/UMIACS University of Maryland College Park MD USA

Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. This paper demonstrates a tool, NetLens, to explore a content-actor paired network data model. The NetLens interface was designed to allow users to pose a series of elementary queries and iteratively refine visual overviews and sorted lists. This enables the support of complex queries that are traditionally hard to specify in node-link visualizations. NetLens is general and scalable in that it applies to any dataset that can be represented with our abstract content-actor data model

关键词： Educational institutions Data visualization computer networks Data models User interfaces Graphical user interfaces Software libraries Law Legal factors computer science

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