research-article

Free access

Data Analytics Anywhere and Everywhere

Author:

Niklas ElmqvistAuthors Info & Claims

Communications of the ACM, Volume 66, Issue 12

Pages 52 - 63

https://doi.org/10.1145/3584858

Published: 17 November 2023 Publication History

All formats PDF

Abstract

Mobile, ubiquitous, and immersive computing appear poised to transform visualization, data science, and data-driven decision making.

References

[1]

Andrews, C. et al. Space to think: Large, high-resolution displays for sensemaking. In Proceedings of the ACM Conf. on Human Factors in Computing Systems. (2010), 55--64

Digital Library

Google Scholar

[2]

Badam, S.K. et al. Supporting visual exploration for Multiple users in large display environments. In Proc. of the IEEE Conf. on Visual Analytics Science and Technology. (2016), 1--10

Crossref

Google Scholar

[3]

Badam, S.K. and Elmqvist, N. PolyChrome: A cross-device framework for collaborative Web visualization. In Proceedings of the ACM Conf. on Interactive Tabletops and Surfaces. (2014), 109--118

Digital Library

Google Scholar

[4]

Badam, S.K. et al. Munin: A peer-to-peer middleware for ubiquitous analytics and visualization spaces. IEEE Trans. on Visualization and Computer Graphics 21, 2 (2015), 215--228

Digital Library

Google Scholar

[5]

Badam, S.K. et al. Vistrates: A component model for ubiquitous analytics. IEEE Trans. on Visualization and Computer Graphics 25, 1 (Jan. 2019), 586--596

Digital Library

Google Scholar

[6]

Bowman, D.A. et al. Information-rich virtual environments: Theory, tools, and research agenda. In Proceedings of the ACM Symp. on Virtual Reality Software and Technology. (2003), 81--90

Digital Library

Google Scholar

[7]

Butcher, P.W.S. et al. VRIA: A Web-based framework for creating immersive analytics experiences. IEEE Trans. on Visualization and Computer Graphics 27, 7 (2021), 3213--3225

Digital Library

Google Scholar

[8]

Butscher, S. et al. Clusters, trends, and outliers: How immersive technologies can facilitate the collaborative analysis of multidimensional data. In Proceedings of the ACM Conf. on Human Factors in Computing Systems. (2018), 1--12

Digital Library

Google Scholar

[9]

Cao, L. Data science: A comprehensive survey. Comput. Surveys 50, 3, Article 43 (2018)

Digital Library

Google Scholar

[10]

Chandler, T. et al. Immersive analytics. In Proceedings of the Intern. Symp. on Big Data Visual Analytics. IEEE, (2015); 1--8

Crossref

Google Scholar

[11]

Clark, A. and Chalmers, D. The extended mind. Analysis 58, 1 (1998), 7--19

Crossref

Google Scholar

[12]

Cordeil, M. et al. IATK: An immersive analytics toolkit. In Proceedings of the IEEE Conf. on Virtual Reality and 3D User Interfaces (2019), 200--209

Crossref

Google Scholar

[13]

Cordeil, M. et al. Immersive axes as embodied affordances for interactive multivariate data visualisation. In Proceedings of the ACM Symp. on User Interface Software and Technology (2017), 71--83

Digital Library

Google Scholar

[14]

Cui, Z. et al. VisHive: Supporting web-based visualization through ad hoc computational clusters of mobile devices. Information Visualization 18, 2 (2019), 195--210

Crossref

Google Scholar

[15]

Dachselt, R. et al. Pico projectors: Firefly or bright future? Interactions 19, 2 (2012), 24--29

Digital Library

Google Scholar

[16]

Dourish, P. Where the Action Is: The Foundations of Embodied Interaction. MIT Press, Cambridge, MA, USA (2001).

Crossref

Google Scholar

[17]

Dourish, P. and Bell, G. Divining a Digital Future---Mess and Mythology in Ubiquitous Computing. MIT Press, Cambridge, MA, USA (2011).

Crossref

Google Scholar

[18]

Elmqvist, N. Embodied human-data interaction. In Proceedings of the ACM CHI Workshop on Embodied Interaction: Theory and Practice in HCI (2011).

Google Scholar

[19]

Elmqvist, N. and Irani, P. Ubiquitous analytics: Interacting with big data anywhere, anytime. IEEE Computer 46, 4 (2013), 86--89

Digital Library

Google Scholar

[20]

ElSayed, N.A.M. et al. Situated analytics: Demonstrating immersive analytical tools with augmented reality. J. of Visual Languages and Computing 36 (2016), 13--23

Digital Library

Google Scholar

[21]

Ens, B. et al. Grand challenges in immersive analytics. In Proceedings of the ACM Conf. on Human Factors in Computing Systems. Article 459 (2021)

Digital Library

Google Scholar

[22]

Fisher, D. et al. Interactions with big data analytics. Interactions 19, 3 (2012), 50--59

Digital Library

Google Scholar

[23]

Fleck, P. et al. RagRug: A toolkit for situated analytics. IEEE Trans. on Visualization and Computer Graphics (2022)

Digital Library

Google Scholar

[24]

Greenberg, S. et al. Proxemic interactions: The new ubicomp? Interactions 18, 1 (2011), 42--50

Digital Library

Google Scholar

[25]

Harrison, C. Appropriated interaction surfaces. IEEE Computer 43, 6 (2010), 86--89

Digital Library

Google Scholar

[26]

Harrison, C. et al. Achieving ubiquity: The new third wave. IEEE Multimedia 17, 3 (2010), 8--12

Digital Library

Google Scholar

[27]

Horak, T. et al. When David meets Goliath: Combining smartwatches with a large vertical display for visual data exploration. In Proceedings of the ACM Conf. on Human Factors in Computing Systems (2018), 19:1--19:13

Digital Library

Google Scholar

[28]

Horak, T. et al. Distributing interactive visualizations in dynamic multi-device setups. In Proceedings of the ACM Conf. on Human Factors in Computing Systems. (2019), 616:1--616:13

Digital Library

Google Scholar

[29]

Hubenschmid, S. et al. Bridging in-situ and ex-situ visual analytics for analyzing mixed reality user studies. In Proceedings of the ACM Conf. on Human Factors in Computing Systems (2022), 24:1--24:20

Digital Library

Google Scholar

[30]

Hutchins, E. Cognition in the Wild. MIT Press, Cambridge, MA, USA (1995).

Google Scholar

[31]

Immersive analytics. Lecture Notes in Computer Science 11190. K. Marriott et al. (eds). Springer Intern. Publishing, Berlin, Germany (2018)

Crossref

Google Scholar

[32]

Kim, K. and Elmqvist, N. Embodied lenses for collaborative visual queries on tabletop displays. Information Visualization 11, 4 (2012), 319--338

Digital Library

Google Scholar

[33]

Kirsh, D. The intelligent use of space. Artificial Intelligence 73, 1--2 (1995), 31--68

Digital Library

Google Scholar

[34]

Kirsh, D. and Maglio, P. On distinguishing epistemic from pragmatic action. Cognitive Science 18, 4 (1994), 513--549

Crossref

Google Scholar

[35]

Klein, G. et al. Making sense of sensemaking: Alternative perspectives. IEEE Intelligent Systems 21, 4 (2006), 70--73

Digital Library

Google Scholar

[36]

Lee, B. et al. Mobile Data Visualization. Chapman and Hall/CRC Press, Boca Raton, FL, USA (2022).

Google Scholar

[37]

Liu, Z. et al. Distributed cognition as a theoretical framework for information visualization. IEEE Trans. on Visualization and Computer Graphics 14, 6 (2008), 1173--1180

Digital Library

Google Scholar

[38]

McGrath, W. et al. Branch-explore-merge: Facilitating real-time revision control in collaborative visual exploration. In Proceedings of the ACM Conf. on Interactive Tabletops and Surfaces (2012), 235--244

Digital Library

Google Scholar

[39]

Russell, D.M. et al. The cost structure of sensemaking. In Proceedings of the ACM Conf. on Human Factors in Computing Systems (1993), 269--276

Digital Library

Google Scholar

[40]

Scaife, M. and Rogers, Y. External cognition: How do graphical representations work? Intern. J. Human-Computer Studies 45, 2 (1996), 185--213

Digital Library

Google Scholar

[41]

Shapiro, L.A. Embodied Cognition. Routledge, New York, NY, USA (2011).

Google Scholar

[42]

Shneiderman, B. Human-Centered AI. Oxford University Press, Oxford, U.K. (2022).

Google Scholar

[43]

Speicher, M. et al. What is mixed reality? In Proceedings of the ACM Conf. on Human Factors in Computing Systems (2019), 537:1--537:15

Digital Library

Google Scholar

[44]

Spindler, M. et al. Tangible views for information visualization. In Proceedings of the ACM Conf. on Interactive Tabletops and Surfaces (2010), 157--166

Digital Library

Google Scholar

[45]

Tukey, J.W. Exploratory Data Analysis. Addison-Wesley, Boston, MA, USA (1977).

Google Scholar

[46]

Weiser, M. The computer for the 21^st century. Scientific American 265, 3 (1991), 94--104

Digital Library

Google Scholar

[47]

Willett, W. et al. Embedded data representations. IEEE Trans. on Visualization and Computer Graphics 23, 1 (Jan. 2017), 461--470

Digital Library

Google Scholar

[48]

Wright, W. et al. The sandbox for analysis: Concepts and valuation. In Proceedings of the ACM Conf. on Human Factors in Computing Systems (2006), 801--810

Digital Library

Google Scholar

Cited By

View all

Quijano-Chavez CDoerr NLee BSchmalstieg DSedlmair M(2024)Brushing and Linking for Situated Analytics2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW62533.2024.00116(597-603)Online publication date: 16-Mar-2024
https://doi.org/10.1109/VRW62533.2024.00116
Shin SBatch AButcher PRitsos PElmqvist N(2024)The Reality of the Situation: A Survey of Situated AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.328554630:8(5147-5164)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3285546
Butcher PBatch ASaffo DMacIntyre BElmqvist NRitsos P(2024)Is Native Naïve? Comparing Native Game Engines and WebXR as Immersive Analytics Development PlatformsIEEE Computer Graphics and Applications10.1109/MCG.2024.336742244:3(91-98)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1109/MCG.2024.3367422
Show More Cited By

Index Terms

Data Analytics Anywhere and Everywhere
1. Human-centered computing

Recommendations

Comments

Information & Contributors

Information

Published In

Communications of the ACM Volume 66, Issue 12

December 2023

88 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3633437

Editor:
James Larus
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 November 2023

Published in CACM Volume 66, Issue 12

Check for updates

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
8,418
Total Downloads

Downloads (Last 12 months)8,418
Downloads (Last 6 weeks)148

Reflects downloads up to 02 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Quijano-Chavez CDoerr NLee BSchmalstieg DSedlmair M(2024)Brushing and Linking for Situated Analytics2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW62533.2024.00116(597-603)Online publication date: 16-Mar-2024
https://doi.org/10.1109/VRW62533.2024.00116
Shin SBatch AButcher PRitsos PElmqvist N(2024)The Reality of the Situation: A Survey of Situated AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.328554630:8(5147-5164)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3285546
Butcher PBatch ASaffo DMacIntyre BElmqvist NRitsos P(2024)Is Native Naïve? Comparing Native Game Engines and WebXR as Immersive Analytics Development PlatformsIEEE Computer Graphics and Applications10.1109/MCG.2024.336742244:3(91-98)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1109/MCG.2024.3367422
Butcher PBatch ARitsos PElmqvist N(2023)Don’t Pull the Balrog — Lessons Learned from Designing Wizualization: a Magic-inspired Data Analytics System in XR2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)10.1109/ISMAR-Adjunct60411.2023.00054(234-237)Online publication date: 16-Oct-2023
https://doi.org/10.1109/ISMAR-Adjunct60411.2023.00054

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Magazine Site

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Big Data Analytics

Big Data Analytics with R and Hadoop

Big Data Analytics with Hadoop 3: Build highly effective analytics solutions to gain valuable insight into your big data