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FacetLens: exposing trends and relationships to support sensemaking within faceted datasets
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Conference on Human Factors in Computing Systems archive
Proceedings of the 27th international conference on Human factors in computing systems table of contents
Boston, MA, USA
SESSION: Visualization 2 table of contents
Pages 1293-1302  
Year of Publication: 2009
ISBN:978-1-60558-246-7
Authors
Bongshin Lee  Microsoft Research, Redmond, WA, USA
Greg Smith  Microsoft Research, Redmond, WA, USA
George G. Robertson  Microsoft Research, Redmond, WA, USA
Mary Czerwinski  Microsoft Research, Redmond, WA, USA
Desney S. Tan  Microsoft Research, Redmond, WA, USA
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Previous research has shown that faceted browsing is effective and enjoyable in searching and browsing large collections of data. In this work, we explore the efficacy of interactive visualization systems in supporting exploration and sensemaking within faceted datasets. To do this, we developed an interactive visualization system called FacetLens, which exposes trends and relationships within faceted datasets. FacetLens implements linear facets to enable users not only to identify trends but also to easily compare several trends simultaneously. Furthermore, it offers pivot operations to allow users to navigate the faceted dataset using relationships between items. We evaluate the utility of the system through a description of insights gained while experts used the system to explore the CHI publication repository as well as a database of funding grant data, and report a formative user study that identified usability issues.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Bungee View, http://cityscape.inf.cs.cmu.edu/bungee
 
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Gapminder, http://www.gapminder.org
 
4
5
 
6
7
 
8
 
9
Microsoft Office Excel, http://office.microsoft.com/enus/excel
 
10
Ranganathan, R. Colon Classification: Basic Classification. Sarada Ranganathan Endowment for Library Science (1991).
 
11
 
12
 
13
Spenke, M. and Beilken, C. InfoZoom - Analysing formula one racing results with an interactive data mining and visualization tool. Proc. Data Mining 2000, (2000), 455--464.
 
14
 
15
 
16
Windows Presentation Foundation (WPF), http://windowsclient.net
 
17
18
 
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Collaborative Colleagues:
Bongshin Lee: colleagues
Greg Smith: colleagues
George G. Robertson: colleagues
Mary Czerwinski: colleagues
Desney S. Tan: colleagues