@comment{{This file has been generated by bib2bib 1.96}}
@comment{{Command line: bib2bib -c '$key="WOODBURY2014A" or $key="BOTTA2012A" or $key="CHEN2013A" or $key="HUANG2014A" or $key="KADIVAR2009A" or $key="DUNSMUIR2010A" or $key="WOODBURY07K" or $key="DUNSMUIR2012A" or $key="CHEN2010A" or 1=2' ./rw.bib}}
  author = {David Botta and Robert Woodbury},
  title = {Predicting topic shift locations in design history},
  journal = {Research in Engineering Design},
  year = 2012,
  key = {Botta},
  volume = {24},
  number = {3},
  pages = {1-14},
  note = {DOI 10.1007/s00163-012-0141-1}
  author = {V. Chen and D. Dunsmuir and S. Alimadadi and E. Lee and
                   J. Guenther and J. Dill and C. Qian and C.D. Shaw and
                   M. Stone and R. Woodbury},
  title = {Model based Interactive Analysis of Interwoven, Imprecise
  key = {Chen},
  booktitle = {Proceedings of IEEE Visual Analytics Science \& Technology},
  pages = {275-276},
  year = 2010,
  address = {Salt Lake City, UT},
  month = {24-29 October},
  publisher = {IEEE},
  note = {VAST 2010 Mini Challenge \#1 Award: Outstanding Interaction
  author = {Chen, Yingjie Victor and Qian, Zhenyu Cheryl and Woodbury, Robert and Dill, John and Shaw, Chris D.},
  title = {Employing a Parametric Model for Analytic Provenance},
  journal = {ACM Trans. Interact. Intell. Syst.},
  issue_date = {April 2014},
  volume = {4},
  number = {1},
  month = apr,
  year = {2014},
  issn = {2160-6455},
  pages = {6:1--6:32},
  articleno = {6},
  numpages = {32},
  url = {http://doi.acm.org/10.1145/2591510},
  doi = {10.1145/2591510},
  acmid = {2591510},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {Dependency graph, analytical reasoning, history, user
                   interaction, visual scripting},
  abstract = {We introduce a propagation-based parametric symbolic model approach to support analytic provenance. This approach combines a script language to capture and encode the analytic process and a parametrically controlled symbolic model to represent and reuse the logic of the analysis process. Our approach first appeared in a visual analytics system called CZSaw. Using a script to capture the analyst's interactions at a meaningful system action level allows creating a parametrically controlled symbolic model in the form of a directed acyclic graph (DAG). Using the DAG allows propagating changes. Graph nodes correspond to variables in CZSaw scripts, which are results (data and data visualizations) generated from user interactions. The user interacts with variables representing entities or relations to create the next step's results. Graph edges represent dependency relationships among nodes. Any change to a variable triggers the propagation mechanism to update downstream dependent variables and in turn updates data views to reflect the change. The analyst can reuse parts of the analysis process by assigning new values to a node in the graph. We evaluated this symbolic model approach by solving three IEEE VAST Challenge contest problems. In each of these challenges, the analyst first created a symbolic model to explore, understand, analyze, and solve a particular sub-problem and then reused the model via its dependency graph propagation mechanism to solve similar sub-problems. With the script and model, CZSaw supports the analytics provenance by capturing, encoding, and reusing the analysis process. The analyst can recall the chronological states of the analysis process with CZSaw script, and may interpret the underlying rationale of the analysis with the symbolic model.}
  author = {D. Dunsmuir and M.Z. Baraghoush and V. Chen and
                   M.E. Joorabchi and S. Alimadadi and
                   E. Lee and J. Dill and C. Qian and C.D. Shaw and
                   R. Woodbury},
  title = {{CZS}aw, {I}MAS \& {T}ableau: Collaboration among Teams},
  key = {Dunsmuir},
  booktitle = {Proceedings of IEEE Visual Analytics Science \& Technology},
  pages = {267-268},
  year = 2010,
  address = {Salt Lake City, UT},
  month = {24-29 October},
  publisher = {IEEE},
  note = {VAST 2010 Grand Challenge Award: Excellent Student Team
  author = {Dustin Dunsmuir and Eric Lee and Chris D. Shaw and Maureen
                   Stone and Robert Woodbury and John Dill},
  title = {A Focus + Context Technique for Visualizing a Document
  key = {Dunsmuir},
  booktitle = {Hawai\'i International Conference on System Sciences},
  pages = {1835-1844},
  year = 2012,
  address = {Maui, Hawai\'i},
  publisher = {IEEE},
  month = {January}
  author = {Dandan Huang and Melanie Tory and Bon Adriel Asenerio and
                   Lyn Bartram and Scott Bateman and Sheelaugh Carpendale and
                   Tony Tang and Robert Woodbury},
  title = {Personal Visualization and Personal Visual Analytics},
  key = {Huang},
  year = 2014,
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  note = {to appear},
  abstract = {Data surrounds each and every one of us in our daily lives,
                   ranging from logs of exercise and diet, to information
                   about our home energy use, to archives of our interactions
                   with others on social media, to online resources pertaining
                   to our hobbies and interests. There is enormous potential
                   for us to collect and use data to understand ourselves
                   better and make positive changes in our
                   lives. Visualization (Vis) and Visual Analytics (VA) offer
                   substantial opportunity to help individuals gain insight
                   and knowledge about themselves and their communities and
                   interests; however, designing and applying visualization
                   tools to support the analysis of data in one’s
                   non-professional life brings a unique set of research and
                   design challenges. We investigate the requirements,
                   characteristics and possible research directions required
                   to take full advantage of Vis and VA in a personal
                   context. First we develop a taxonomy of design dimensions
                   based on research in the literature to provide a coherent
                   vocabulary for discussing Personal Visualization and
                   Personal Visual Analytics (PV&PVA). We then examine where
                   the systems from the literature fit with respect to the
                   design dimensions. By identifying and exploring clusters
                   within the above design space, we discuss challenges and
                   share our perspectives on future research. This work brings
                   together research that was previously scattered across
                   several disciplines. Our goal is to call research attention
                   to this space and engage the visualization community to
                   explore the enabling techniques and technology that will
                   support people to better understand data relevant to their
                   personal lives, interests, and needs.}
  author = {Nazanin Kadivar and Victor Chen and Dustin Dunsmuir and
                   Eric Lee and Cheryl Qian and John Dill and Christopher Shaw
                   and Robert Woodbury},
  title = {Capturing and Supporting the Analysis Process},
  key = {Kadivar},
  booktitle = {{P}roceedings of {IEEE} {V}isual {A}nalytics {S}cience and {T}echnology},
  organization = {IEEE, Atlantic City, NJ},
  month = {October 11-16},
  pages = {131-138},
  year = 2009,
  annote = {\relax\par {\bfseries Abstract:}
                   \emph{Visual analytics tools provide powerful visual
                   representations in order to support the sense-making
                   process. In this process, analysts typically iterate
                   through sequences of steps many times, varying parameters
                   each time. Few visual analytics tools support this process
                   well, nor do they provide support for visualizing and
                   understanding the analysis process itself. To help analysts
                   understand, explore, reference, and reuse their analysis
                   process, we present a visual analytics system named {CZS}aw
                   (See-Saw) that provides an editable and re-playable history
                   navigation channel in addition to multiple visual
                   representations of document collections and the entities
                   within them (in a manner inspired by Jigsaw
                   [24]). Conventional history navigation tools range from
                   basic undo and redo to branching timelines of user
                   actions. In {CZS}aw’s approach to this, first, user
                   interactions are translated into a script language that
                   drives the underlying scripting-driven propagation
                   system. The latter allows analysts to edit analysis steps,
                   and ultimately to program them. Second, on this base, we
                   build both a history view showing progress and alternative
                   paths, and a dependency graph showing the underlying logic
                   of the analysis and dependency relations among the results
                   of each step. These tools result in a visual model of the
                   sense-making process, providing a way for analysts to
                   visualize their analysis process, to reinterpret the
                   problem, explore alternative paths, extract analysis
                   patterns from existing history, and reuse them with other
                   related analyses.}}
  author = {Yingjie (Victor) Chen and Zhenyu (Cheryl) Qian and Robert Woodbury},
  booktitle = {CAADFutures 2007},
  key = {Chen},
  month = {July},
  organization = {CAADFutures Foundation},
  pages = {403-416},
  title = {Local Navigation can Reveal Implicit Relations},
  year = 2007
  author = {Eric Lee and Ankit Gupta and David Darvill and John Dill
                   and Christopher D Shaw and Robert Woodbury},
  title = {The {CZS}aw {N}otes Case Study},
  key = {Lee},
  booktitle = {Visualization and Data Analysis (VDA 2014)},
  year = 2014,
  month = {February},
  organization = {SPIE},
  pages = {901706-14},
  doi = {10.1117/12.2041318},
  url = { http://dx.doi.org/10.1117/12.2041318},
  abstract = {Analysts need to keep track of their analytic findings,
                   observations, ideas, and hypotheses throughout the analysis
                   process. While some visual analytics tools support such
                   note-taking needs, these notes are often represented as
                   objects separate from the data and in a workspace separate
                   from the data visualizations. Representing notes the same
                   way as the data and integrating them with data
                   visualizations can enable analysts to build a more cohesive
                   picture of the analytical process. We created a note-taking
                   functionality called CZNotes within the visual analytics
                   tool CZSaw for analyzing unstructured text
                   documents. CZNotes are designed to use the same model as
                   the data and can thus be visualized in CZSaw's existing
                   data views.  We conducted a preliminary case study to
                   observe the use of CZNotes and observed that CZNotes has
                   the potential to support progressive analysis, to act as a
                   shortcut to the data, and supports creation of new data

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