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ActiveInk: (Th)Inking with Data

(Th)Inking with Data
Hugo Romat; Nathalie Henry Riche; Ken Hinckley; Bongshin Lee; Caroline Appert; Emmanuel Pietriga; Christopher Collins;

ActiveInk: (Th)Inking with Data

Abstract

International audience; During sensemaking, people annotate insights: underlining sentences in a document or circling regions on a map. They jot down their hypotheses: drawing correlation lines on scatterplots or creating personal legends to track patterns. We present ActiveInk, a system enabling people to seamlessly transition between exploring data and externalizing their thoughts using pen and touch. ActiveInk enables the natural use of pen for active reading behaviors, while supporting analytic actions by activating any of these ink strokes. Through a qualitative study with eight participants, we contribute observations of active reading behaviors during data exploration and design principles to support sensemaking.

Country
France
Subjects by Vocabulary

Microsoft Academic Graph classification: Design elements and principles Visualization Computer science Human–computer interaction Transition (fiction) Annotation Sensemaking Qualitative research Natural (music)

Keywords

Touch screens, Active reading, Visualization systems and tools, Ink, Annotation, Visualization, Digital pen, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]

41 references, page 1 of 5

[1] Christopher Andrews, Alex Endert, and Chris North. 2010. Space to Think: Large High-resolution Displays for Sensemaking. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems. ACM, 55-64. https://doi.org/10.1145/1753326.1753336

[2] Lisa Anthony and Jacob O. Wobbrock. 2012. $N-protractor: A Fast and Accurate Multistroke Recognizer. In Proc. Graphics Interface. Canadian Information Processing Society, 117-120.

[3] Caroline Appert, Michel Beaudouin-Lafon, and Wendy E. Mackay. 2005. Context matters: Evaluating Interaction Techniques with the CIS Model. In People and Computers XVIII -Design for Life, Sally Fincher, Panos Markopoulos, David Moore, and Roy Ruddle (Eds.). Springer, London, 279-295. https://doi.org/10.1007/1-84628-062-1_18

[4] Michael Bostock, Vadim Ogievetsky, and Jefrey Heer. 2011. D3 DataDriven Documents. IEEE Trans. on Visualization and Computer Graphics 17, 12 (Dec. 2011), 2301-2309. https://doi.org/10.1109/TVCG.2011.185

[5] Mauro Cherubini, Gina Venolia, Rob DeLine, and Andrew J. Ko. 2007. Let's Go to the Whiteboard: How and Why Software Developers Use Drawings. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems. ACM, 557-566. https://doi.org/10.1145/1240624.1240714

[6] Christopher Collins, Gerald Penn, and Sheelagh Carpendale. 2009. Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations. IEEE Trans. on Visualization and Computer Graphics 15, 6 (2009), 1009-1016. https://doi.org/10.1109/TVCG.2009.122

[7] Andrew Crotty, Alex Galakatos, Emanuel Zgraggen, Carsten Binnig, and Tim Kraska. 2015. Vizdom: Interactive analytics through pen and touch. Proc. of the VLDB Endowment 8, 12 (2015), 2024-2027. https://doi.org/10.14778/2824032.2824127

[8] Steven M. Drucker, Danyel Fisher, Ramik Sadana, Jessica Herron, and m.c. schraefel. 2013. TouchViz: A Case Study Comparing Two Interfaces for Data Analytics on Tablets. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems. ACM, 2301-2310. https: //doi.org/10.1145/2470654.2481318

[9] Ken Hinckley, Xiaojun Bi, Michel Pahud, and Bill Buxton. 2012. Informal information gathering techniques for active reading. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems. ACM, 1893-1896. https://doi.org/10.1145/2207676.2208327

[10] Ken Hinckley, Ken Hinckley, Shengdong Zhao, Raman Sarin, Patrick Baudisch, Edward Cutrell, Michael Shilman, and Desney Tan. 2007. InkSeine: In Situ Search for Active Note Taking. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems. ACM, 251-260. https: //doi.org/10.1145/1240624.1240666

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
24
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