Cooperative visualization: a design case
| DOI | https://doi.org/10.1108/07378831311329112 |
| Published date | 07 June 2013 |
| Date | 07 June 2013 |
| Pages | 371-390 |
| Author | Nathan Prestopnik |
| Subject Matter | Information & knowledge management,Library & information science |
Cooperative visualization:
a design case
Nathan Prestopnik
School of Information Studies, Syracuse University, Syracuse, New York, USA
Abstract
Purpose – In this design case, a participatory approach to visualizing a complex computational
pipeline was adopted, with the goal of exploring what benefits might be derived when groups of people
visualize complex information for themselves.
Design/methodology/approach – Several visualization artifacts were developed to support
collaborative process at the Laser In terferometer Gravitational Wa ve Observatory (LIGO).
Researchers adopted a participatory approach, engaging directly in LIGO activities and drawing
together explicitly codified data from the LIGO computational pipeline as well as structural knowledge
tacitly held by project scientists. Both sources of information were critical to producing meaningful
visualizations and progressing design and research efforts.
Findings – This design case revealed several benefits realized when individuals or groups visualize
information for themselves, especially improved communication and enhanced understanding of
complex systems of information.
Originality/value – This design case demonstrates how cooperatively creating visualizations can
enhance understanding and support group activities and goals. It is also a call to move beyond data,
technologies, and techniques to introduce more human-centered approaches within visualization
scholarship.
Keywords Information visualization, Interaction, Design, Cooperativework, Information management
Paper type Case study
Introduction
In the current body of information visualization (IV) literature, there are two broad
emphases: representation and interaction (Yi et al., 2007). Representation research
explores ways to visually represent information on display devices, including
algorithms, techniques, and technologies (e.g. Card et al., 1991; Feiner and Clifford,
1990; Fekete and Plaisant, 2002; Furnas, 1986; Mackinlay et al., 1991; Robertson et al.,
1991). Interaction research studies the dialog that occurs between users of a visual
information system and the system itself. Interactions might include filtering data to
order it in various ways, drilling down through a display to different levels of detail,
zooming, panning, or otherwise manipulating the visual display to achieve the view or
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0737-8831.htm
Published with the kind permission of iSchools, http://ischools.org
A version of this article was originally presented at the iConference 2013 held in Fort Worth,
Texas, 12-15 February 2013.
The author would like to acknowledge Duncan Brown, Peter Couvares, Steven Rowe, and
Howard Turtle, co-designers involved in the visualization process described in this work. The
author would also like to thank the Syracuse University Gravitational Wave Group and the
Syracuse University Center for Natural Language Processing for participation and insights over
the course of developing this design case. This work represents the author’s view and has not
been reviewed by the LIGO Scientific Collaboration.
Cooperative
visualization
371
Received 21 February 2013
Revised 21 February 2013
Accepted 21 February 2013
Library Hi Tech
Vol. 31 No. 2, 2013
pp. 371-390
qEmerald Group Publishing Limited
0737-8831
DOI 10.1108/07378831311329112
perspective that the user is interested in. Of the two emphases, interaction and
representation, representation has received by far the most scholarly attention from IV
researchers (Yi et al., 2007; Ellis and Dix, 2006; Tory and Mo
¨ller, 2004; Thomas and
Cook, 2005; Chen and Czerwinski, 2000; Chen, 2005), but interaction is of growi ng
interest in the IV community (Chen, 2005). The focus of this present research is on
interaction.
Visualization interactions can be split into three categories:
(1) representational interactions;
(2) cognitive interactions; and
(3) creative interactions.
Representational interactions include the many types already mentioned; they provide
the user opportunities to modify the visual display and organization of information on
the screen. Cognitive interactions, described through a variety of models and theories
of visualization (e.g. Spence, 2001, 2007; Ware, 2004; Chen, 2003, 2006; Card et al., 1999),
are the purely mental activities that a user will undertake when working with a
visualization, setting aside any affordances the visualization artifact itself might have
for manipulating the representation of data.
Creative interactions are defined by human involvement in the creative activity of
generating visualizations, with the term “creative” indicating the act of creation – the
transformation of raw information into a visual representation. Creative interactions
can be highly complex and require a great deal of cognitive effort (for example,
manually transforming qualitative or quantitative information into something that can
be visualized). Creative interactions are less well studied than other forms of
interaction, but they are important. Interactions that result in the formation of a
visualization will necessarily require individuals to cognitively and representationally
interact with raw information and the visualization itself. The same is true of groups of
individuals who cooperate together to produce visualizations, but with additional
possible benefits, including the use of collaboratively developed visualizations to foster
discussion and achieve group goals.
Broadly, this present research explores the following: What do groups of people
gain by visualizing information for themselves? There are many possibilities,
including cognitive or analytical benefits, the ability to cooperate and share
information more effectively, and the ability to draw new connections or see new
patterns within one or more data sets. Using participatory research techniques, these
possibilities are explored in a design case: the documented effort to visualize a complex
computational pipeline used by astronomers for signal/noise processing of
gravitational wave detector data. The different visualizations described in this
design case were produced under the aegis of ongoing development of an information
retrieval (IR) system to be used by gravitational wave physicists as part of their work.
Key users of the visualizations included the astronomers involved in the Laser
Interferometer Gravitational Wave Observatory (LIGO) collaboration as well as the IR
researchers responsible for producing the retrieval system for this collaboration and
the IV researcher (the author) tasked with studying various use scenarios and creating
the visualizations themselves.
The LIGO design case revealed a number of concepts that are important elements of
creative visualization interactions. Tacit and explicit knowledge (Collins, 2007;
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