pith. sign in

arxiv: 2606.11980 · v1 · pith:XH7CIW6Enew · submitted 2026-06-10 · 💻 cs.HC

Somewhere Over the Desktop: A Research Agenda for Ubiquitous Analytics

Niklas Elmqvist , Panagiotis D. Ritsos , Peter W. S. Butcher This is my paper

Pith reviewed 2026-06-27 08:24 UTC · model grok-4.3

classification 💻 cs.HC
keywords ubiquitous analyticsspatial computinggenerative AIdata visualizationresearch agendahuman-computer interactionwearable displayscollaboration
0
0 comments X

The pith

Spatial computing, generative AI, and open web standards converge to enable data sensemaking on distributed devices.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that advances in spatial operating systems, agentic AI, and open web standards create opportunities for ubiquitous analytics, defined as the use of many physically distributed, networked devices to support data sensemaking anytime and anywhere. A sympathetic reader would care because proprietary platforms risk settling design conventions that limit these possibilities without evidence-based research alternatives. The authors trace the field's intellectual history as a structured genealogy organized into seven clusters. Crossing the clusters produces 42 research challenges to guide future work.

Core claim

Ubiquitous analytics has matured to the point where its intellectual history can be read as a structured genealogy of foundations, contributions, and lineages. The authors organize this genealogy into clusters spanning cognition, context, interaction, platforms, visualization, collaboration, and evaluation. Crossing these clusters yields a total of 42 future research challenges that address the convergence of spatial computing, generative AI, and open web standards.

What carries the argument

A structured genealogy of foundations, contributions, and lineages organized into seven clusters that are crossed to produce 42 research challenges.

If this is right

  • Data sensemaking becomes possible anytime and anywhere using physically distributed, networked devices.
  • Agentic AI can operate on the same spatial substrates as the human user.
  • Evidence-based alternatives from research can prevent proprietary platforms from locking in design conventions.
  • Visualization and collaboration gain new capabilities in spatial and wearable environments.
  • Evaluation methods must adapt to distributed, multi-device spatial settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The agenda could extend analytics tools to everyday physical environments such as homes or collaborative workspaces.
  • It connects visualization research directly to emerging standards in wearable and augmented displays.
  • A testable extension would involve building an open prototype for one challenge and measuring changes in sensemaking speed or accuracy.
  • Distributed device networks raise privacy issues that may need dedicated follow-on challenges.

Load-bearing premise

Proprietary platforms are settling design conventions that will calcify without evidence-based alternatives from the research community.

What would settle it

An observation that design conventions in Android XR, Meta Horizon OS, or Apple visionOS have incorporated open research contributions or that open alternatives have achieved measurable adoption in spatial analytics tools.

Figures

Figures reproduced from arXiv: 2606.11980 by Niklas Elmqvist, Panagiotis D. Ritsos, Peter W. S. Butcher.

Figure 1
Figure 1. Figure 1: Lineage plots. Left: an ancestry matrix where each row is a contribution, columns are contributions with descendants, and a dot at (row, column) means the row descends from that column; a connector joins a row’s ancestors, and each dot is colored by its ancestor’s cluster. Right: a timeline panel sharing the row order, with active spans on a continuous time axis and arrows for ongoing work. 3 METHOD Our ap… view at source ↗
Figure 2
Figure 2. Figure 2: Pre-2000 lineage. Foundational work for ubiquitous analytics. Three families dominate the roots: a visualization line from Bertin’s visual variables and Stevens’ scales of measurement through the reference pipeline; an interaction line from Shneiderman’s direct manipulation and Norman’s gulfs; and a cognition line from Hutchins’ distributed cognition. Sutherland’s reality–virtuality lineage and Weiser’s ub… view at source ↗
Figure 3
Figure 3. Figure 3: 2000–2012 lineage. The second period, in which the visualization family consolidates and the cognition family turns toward analysis. Visual analytics arrives; visualization grammars take hold; interaction gains its own theory. The thread that matters most for the present agenda is the cognition spine running from distributed cognition for visualization through the Sandbox to Space to Think. On the platform… view at source ↗
Figure 4
Figure 4. Figure 4: 2013–2022 lineage. The decade ubiquitous analytics names itself and then splits into the subcommunities the field still works in. Ubiquitous analytics is coined; immersive analytics consolidates as a sibling; situated analytics splits off. The platform substrate matters here, with the engine-native toolkits built on Unity and VRIA opening the web-versus-engine split. The period closes with the first attemp… view at source ↗
Figure 5
Figure 5. Figure 5: 2022-2026 lineage. The most recent period where theory consolidates and the platform ground shifts. Data analytics as an anywhere and everywhere paradigm restates UA as a program; interaction substrates supply the term the framework runs on; DashSpace and Spatialstrates turn substrates into working platforms on open web standards. e Measuring cognition in the field. How do we measure distributed analytical… view at source ↗
read the original abstract

Spatial computing, generative AI, and open web standards are converging. Three spatial operating systems -- Android XR, Meta Horizon OS, and Apple visionOS -- now ship with platform-level scene understanding. Wearable displays span the range from full headsets to slim smartglasses. Agentic AI operates on the same spatial substrates as the human user. This convergence enables new opportunities for \textit{ubiquitous analytics} (UA): the use of many, physically distributed, networked devices to support data sensemaking anytime and anywhere. But proprietary platforms are settling design conventions that will calcify without evidence-based alternatives. UA has now matured to the point where its intellectual history can be read as a structured genealogy of foundations, contributions, and lineages. We trace this genealogy and organize it into clusters spanning cognition, context, interaction, platforms, visualization, collaboration, and evaluation. Finally, we cross these clusters with each other, yielding a total of 42 future research challenges.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that spatial computing (e.g., Android XR, Meta Horizon OS, Apple visionOS), generative AI, and open web standards are converging to enable ubiquitous analytics (UA): the use of many physically distributed, networked devices to support data sensemaking anytime and anywhere. It traces UA's intellectual genealogy, organizes it into seven clusters (cognition, context, interaction, platforms, visualization, collaboration, evaluation), and derives 42 cross-cluster research challenges. The paper warns that proprietary platforms risk calcifying design conventions without evidence-based research alternatives.

Significance. If the synthesis holds, the paper supplies a structured research agenda that could usefully guide HCI and visualization work on multi-device sensemaking. The explicit enumeration of 42 challenges is a concrete contribution that offers the community specific directions rather than vague calls for more research. The paper's organization across seven clusters provides a useful map of the intellectual lineage.

major comments (2)
  1. [Abstract] Abstract: the claim that UA 'has now matured to the point where its intellectual history can be read as a structured genealogy' and that the 42 challenges 'follow rigorously from the synthesis' is not supported by any description of the method used to identify the seven clusters, validate them, or perform the cross-cluster derivation; this methodology is load-bearing for the central contribution.
  2. [Abstract] Abstract: the enabling claim that the three technologies 'converge' to support UA via 'agentic AI [operating] on the same spatial substrates' is asserted without concrete examples, citations, or evidence of interaction between scene understanding, generative models, and open standards; this underpins the motivation for the entire agenda.
minor comments (1)
  1. [Abstract] Abstract: 'agentic AI' is introduced without a brief definition or reference; adding one sentence would improve readability for the broader HCI audience.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The two major comments identify important gaps in the abstract's grounding and methodological transparency. We agree that both points require revision and outline specific changes below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that UA 'has now matured to the point where its intellectual history can be read as a structured genealogy' and that the 42 challenges 'follow rigorously from the synthesis' is not supported by any description of the method used to identify the seven clusters, validate them, or perform the cross-cluster derivation; this methodology is load-bearing for the central contribution.

    Authors: We agree that the abstract's claims about a 'structured genealogy' and challenges that 'follow rigorously from the synthesis' require explicit methodological support, as this underpins the paper's central contribution. The current manuscript describes the clusters and challenges in the body but does not detail the derivation process in the abstract or provide a dedicated methods subsection. In revision we will (1) shorten the abstract's methodological claim and (2) add a new subsection (likely 1.3) that explains the process: a systematic literature review of foundational papers in each domain, thematic clustering validated through author consensus and alignment with prior surveys, and systematic cross-cluster pairing to generate the 42 challenges. This will make the synthesis reproducible and address the load-bearing concern. revision: yes

  2. Referee: [Abstract] Abstract: the enabling claim that the three technologies 'converge' to support UA via 'agentic AI [operating] on the same spatial substrates' is asserted without concrete examples, citations, or evidence of interaction between scene understanding, generative models, and open standards; this underpins the motivation for the entire agenda.

    Authors: The referee is correct that the abstract asserts convergence of spatial computing, generative AI, and open standards without citations or concrete interaction examples. While the body of the manuscript discusses these technologies, the abstract must be self-contained. We will revise the abstract to include brief citations to platform documentation (Apple visionOS scene understanding, Meta Horizon OS spatial APIs, Android XR) and recent work on agentic models operating over spatial representations, plus one concrete example of interaction (e.g., scene-understanding output feeding generative models that produce device-agnostic visualization specifications via open web standards). This grounds the motivation without lengthening the abstract excessively. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper is a position and synthesis piece that traces an intellectual genealogy of ubiquitous analytics across seven clusters and proposes 42 research challenges. It advances no derivations, equations, predictions, fitted parameters, or formal propositions. All claims rest on external literature synthesis rather than any self-referential reduction or internal construction, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is a position and review paper with no mathematical derivations, data fitting, or new entities postulated. The central premise rests on the domain assumption that the field has matured enough for a structured genealogy and that proprietary platforms require research alternatives.

axioms (1)
  • domain assumption The field of ubiquitous analytics has matured to the point where its intellectual history can be read as a structured genealogy of foundations, contributions, and lineages.
    Invoked in the abstract as the justification for tracing the genealogy and organizing into clusters.

pith-pipeline@v0.9.1-grok · 5705 in / 1124 out tokens · 27726 ms · 2026-06-27T08:24:18.224791+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

88 extracted references · 1 linked inside Pith

  1. [1]

    Ubiquitous analytics: Interacting with big data anywhere, anytime

    N. Elmqvist and P . Irani, “Ubiquitous analytics: Interacting with big data anywhere, anytime.”IEEE Computer, vol. 46, no. 4, pp. 86–89, 2013

    2013

  2. [2]

    The computer for the 21st Century,

    M. Weiser, “The computer for the 21st Century,”Scientific Ameri- can, vol. 265, no. 3, pp. 94–104, 1991

    1991

  3. [3]

    Data analytics anywhere and everywhere,

    N. Elmqvist, “Data analytics anywhere and everywhere,”Commu- nications of the ACM, vol. 66, no. 12, pp. 52–63, 2023

    2023

  4. [4]

    Hutchins,Cognition in the Wild

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

    1995

  5. [5]

    Distributed cogni- tion: toward a new foundation for human-computer interaction research,

    J. D. Hollan, E. L. Hutchins, and D. Kirsh, “Distributed cogni- tion: toward a new foundation for human-computer interaction research,”ACM Transactions on Computer-Human Interaction, vol. 7, no. 2, pp. 174–196, 2000

    2000

  6. [6]

    Is native na¨ıve? comparing native game engines and WebXR as immersive analytics development platforms,

    P . W. S. Butcher, A. Batch, D. Saffo, B. MacIntyre, N. Elmqvist, and P . D. Ritsos, “Is native na¨ıve? comparing native game engines and WebXR as immersive analytics development platforms,”IEEE Computer Graphics & Applications, vol. 44, no. 3, pp. 91–98, 2024

    2024

  7. [7]

    What’s the situation with situated visualization? a survey and perspectives on situatedness,

    N. Bressa, H. Korsgaard, A. Tabard, S. Houben, and J. Vermeulen, “What’s the situation with situated visualization? a survey and perspectives on situatedness,”IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 107–117, 2022

    2022

  8. [8]

    Grand challenges in immersive analytics,

    B. Ens, B. Bach, M. Cordeil, U. Engelke, M. Serrano, W. Wil- lett, A. Prouzeau, C. Anthes, W. B ¨uschel, C. Dunne, T. Dwyer, J. Grubert, J. H. Haga, N. Kirshenbaum, D. Kobayashi, T. Lin, M. Olaosebikan, F. Pointecker, D. Saffo, N. Saquib, D. Schmalstieg, D. A. Szafir, M. Whitlock, and Y. Yang, “Grand challenges in immersive analytics,” inProceedings of th...

    2021

  9. [9]

    Visualization beyond the desktop—the next big thing,

    J. Roberts, P . Ritsos, S. K. Badam, D. Brodbeck, J. Kennedy, and N. Elmqvist, “Visualization beyond the desktop—the next big thing,”IEEE Computer Graphics & Applications, vol. 34, no. 6, pp. 26–34, Nov 2014

    2014

  10. [10]

    Achieving ubiquity: The new third wave,

    C. Harrison, J. Wiese, and A. K. Dey, “Achieving ubiquity: The new third wave,”IEEE Multimedia, vol. 17, no. 3, pp. 8–12, 2010

    2010

  11. [11]

    Immersive analytics,

    T. Chandler, M. Cordeil, T. Czauderna, T. Dwyer, J. Glowacki, C. Goncu, M. Klapperstueck, K. Klein, F. Schreiber, and E. Wilson, “Immersive analytics,” inProceedings of the International Symposium on Big Data Visual Analytics. Piscataway, NJ, USA: IEEE, 2015, pp. 1–8

    2015

  12. [12]

    Marriott, F

    K. Marriott, F. Schreiber, T. Dwyer, K. Klein, N. H. Riche, T. Itoh, W. Stuerzlinger, and B. H. Thomas, Eds.,Immersive Analytics, ser. Lecture Notes in Computer Science. Berlin, Germany: Springer International Publishing, 2018, vol. 11190

    2018

  13. [13]

    Situated analytics: Demonstrating immersive analyt- ical tools with Augmented Reality,

    N. A. M. ElSayed, B. H. Thomas, K. Marriott, J. Piantadosi, and R. T. Smith, “Situated analytics: Demonstrating immersive analyt- ical tools with Augmented Reality,”Journal of Visual Languages & Computing, vol. 36, pp. 13–23, 2016

    2016

  14. [14]

    Embedded data represen- tations,

    W. Willett, Y. Jansen, and P . Dragicevic, “Embedded data represen- tations,”IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 461–470, 2017

    2017

  15. [15]

    B. Lee, R. Dachselt, P . Isenberg, and E. K. Choe,Mobile Data Visualization, ser. AK Peters Visualization Series. Boca Raton, FL, USA: Chapman and Hall/CRC Press, 2022

    2022

  16. [16]

    External cognition: how do graphical representations work?

    M. Scaife and Y. Rogers, “External cognition: how do graphical representations work?”International Journal of Human-Computer Studies, vol. 45, no. 2, pp. 185–213, 1996

    1996

  17. [17]

    Distributed cognition as a theoretical framework for information visualization,

    Z. Liu, N. J. Nersessian, and J. T. Stasko, “Distributed cognition as a theoretical framework for information visualization,”IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 6, pp. 1173–1180, 2008. 13

    2008

  18. [18]

    Instrumental interaction: an interaction model for designing post-WIMP user interfaces,

    M. Beaudouin-Lafon, “Instrumental interaction: an interaction model for designing post-WIMP user interfaces,” inProceedings of the ACM Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2000, pp. 446–453

    2000

  19. [19]

    Designing interaction, not interfaces,

    ——, “Designing interaction, not interfaces,” inProceedings of the ACM Conference on Advanced Visual Interfaces. New York, NY, USA: ACM, 2004, pp. 15–22

    2004

  20. [20]

    Interaction substrates: Combining power and simplicity in interactive systems,

    W. E. Mackay and M. Beaudouin-Lafon, “Interaction substrates: Combining power and simplicity in interactive systems,” inPro- ceedings of the ACM Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2025, pp. 6871:1–687:16

    2025

  21. [21]

    Cognitive engineering,

    D. A. Norman, “Cognitive engineering,” inUser Centered System Design: New Perspectives on Human-Computer Interaction, D. A. Norman and S. W. Draper, Eds. Hillsdale, NJ, USA: Lawrence Erlbaum Associates, 1986, pp. 31–61

    1986

  22. [23]

    Grand challenges for HCI researchers,

    B. Shneiderman, C. Plaisant, M. S. Cohen, S. Jacobs, N. Elmqvist, and N. Diakopoulos, “Grand challenges for HCI researchers,” Interactions, vol. 23, no. 5, pp. 24–25, 2016

    2016

  23. [24]

    J. J. Thomas and K. A. Cook, Eds.,Illuminating the Path: The Re- search and Development Agenda for Visual Analytics. Los Alamitos, CA, USA: IEEE Computer Society, 2005

    2005

  24. [25]

    Top scientific visualization research problems,

    C. R. Johnson, “Top scientific visualization research problems,” IEEE Computer Graphics & Applications, vol. 24, no. 4, pp. 13–17, 2004

    2004

  25. [26]

    Guidelines for snowballing in systematic literature studies and a replication in software engineering,

    C. Wohlin, “Guidelines for snowballing in systematic literature studies and a replication in software engineering,” inProceedings of the ACM Conference on Evaluation and Assessment in Software Engineering. New York, NY, USA: ACM, 2014

    2014

  26. [27]

    Connectivity in a citation network: The development of dna theory,

    N. P . Hummon and P . Dereian, “Connectivity in a citation network: The development of dna theory,”Social Networks, vol. 11, no. 1, pp. 39–63, 1989

    1989

  27. [28]

    The reality of the situation: A survey of situated analytics,

    S. Shin, A. Batch, P . W. S. Butcher, P . D. Ritsos, and N. Elmqvist, “The reality of the situation: A survey of situated analytics,”IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 8, pp. 5147–5164, 2024

    2024

  28. [29]

    Bertin,S´ emiologie graphique : Les diagrammes - Les r´ eseaux - Les cartes, les r´eimpressions ed

    J. Bertin,S´ emiologie graphique : Les diagrammes - Les r´ eseaux - Les cartes, les r´eimpressions ed. Paris, France: Editions de l’Ecole des Hautes Etudes en Sciences, 1967

    1967

  29. [30]

    UpSet: Visualization of intersecting sets,

    A. Lex, N. Gehlenborg, H. Strobelt, R. Vuillemot, and H. Pfister, “UpSet: Visualization of intersecting sets,”IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1983–1992, 2014

    1983

  30. [31]

    GeneaQuilts: A system for exploring large genealogies,

    A. Bezerianos, P . Dragicevic, J. Fekete, J. Bae, and B. Watson, “GeneaQuilts: A system for exploring large genealogies,”IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1073–1081, 2010

    2010

  31. [32]

    On the theory of scales of measurement,

    S. S. Stevens, “On the theory of scales of measurement,”Science, vol. 103, no. 2684, pp. 677–680, 1946

    1946

  32. [33]

    Graphical perception: Theory, experimentation and application to the development of graphical methods,

    W. S. Cleveland and R. McGill, “Graphical perception: Theory, experimentation and application to the development of graphical methods,”Journal of the American Statistical Association, vol. 79, no. 387, pp. 531–554, Sep. 1984

    1984

  33. [34]

    Automating the design of graphical presentations of relational information,

    J. Mackinlay, “Automating the design of graphical presentations of relational information,”ACM Transactions on Graphics, vol. 5, no. 2, pp. 110–141, Apr. 1986

    1986

  34. [35]

    S. K. Card, J. D. Mackinlay, and B. Shneiderman, Eds.,Readings in Information Visualization: Using Vision to Think. San Francisco, CA, USA: Morgan Kaufmann Publishers, 1999

    1999

  35. [36]

    A taxonomy of visualization techniques using the data state reference model,

    E. H. Chi, “A taxonomy of visualization techniques using the data state reference model,” inProceedings of the IEEE Symposium on Information Visualization. Los Alamitos, CA, USA: IEEE Computer Society, 2000, pp. 69–75

    2000

  36. [37]

    Direct manipulation: A step beyond program- ming languages,

    B. Shneiderman, “Direct manipulation: A step beyond program- ming languages,”Computer, vol. 16, no. 8, pp. 57–69, 1983

    1983

  37. [38]

    Dynamic queries for visual information seeking,

    ——, “Dynamic queries for visual information seeking,”IEEE Software, vol. 11, no. 6, pp. 70–77, Nov. 1994

    1994

  38. [39]

    The eyes have it: A task by data type taxonomy for information visualizations,

    ——, “The eyes have it: A task by data type taxonomy for information visualizations,” inProceedings of the IEEE Symposium on Visual Languages. Piscataway, NJ, USA: IEEE, 1996, pp. 336– 343

    1996

  39. [40]

    The intelligent use of space,

    D. Kirsh, “The intelligent use of space,”Artificial Intelligence, vol. 73, no. 1-2, pp. 31–68, 1995

    1995

  40. [41]

    L. S. Vygotsky,Mind in Society: The Development of Higher Psycho- logical Processes. Boston, MA, USA: Harvard University Press, 1978

    1978

  41. [42]

    B. A. Nardi, Ed.,Context and Consciousness: Activity Theory and Human-Computer Interaction. Cambridge, MA, USA: MIT Press, 1995

    1995

  42. [43]

    Kaptelinin and M

    V . Kaptelinin and M. Czerwinski, Eds.,Beyond the Desktop Metaphor: Design Integrated Digital Work Environments. Cambridge, MA, USA: MIT Press, 2009

    2009

  43. [44]

    Information foraging,

    P . Pirolli and S. Card, “Information foraging,”Psychological Review, vol. 106, pp. 643–675, 1999

    1999

  44. [45]

    The ultimate display,

    I. E. Sutherlandet al., “The ultimate display,” inProceedings of the IFIP Congress, vol. 2, 1965, pp. 506–508

    1965

  45. [46]

    A Taxonomy of Mixed Reality Visual Displays,

    P . Milgram and F. Kishino, “A Taxonomy of Mixed Reality Visual Displays,”IEICE Transactions on Information Systems, vol. E77-D, no. 12, pp. 1321–1329, 1994, pp. 1321–1329

    1994

  46. [47]

    A survey of augmented reality,

    R. T. Azuma, “A survey of augmented reality,”Presence: Teleopera- tors and Virtual Environments, vol. 6, no. 4, pp. 355–385, 1997

    1997

  47. [48]

    D. Keim, G. Andrienko, J.-D. Fekete, C. G ¨org, J. Kohlhammer, and G. Melancon,Visual Analytics: Definition, Process, and Challenges, ser. LNCS. Springer International Publishing, 2008, vol. 4950, ch. 7, pp. 154–175

    2008

  48. [49]

    The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis,

    P . Pirolli and S. Card, “The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis,” inProceedings of the International Conference on Intelligence Analysis, vol. 5, 2005, pp. 2–4

    2005

  49. [50]

    The cost structure of sensemaking,

    D. M. Russell, M. J. Stefik, P . Pirolli, and S. K. Card, “The cost structure of sensemaking,” inProceedings of the ACM Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 1993, pp. 269–276

    1993

  50. [51]

    Toward a deeper understand- ing of the role of interaction in information visualization,

    J. S. Yi, Y. ah Kang, and J. Stasko, “Toward a deeper understand- ing of the role of interaction in information visualization,”IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1224–1231, 2007

    2007

  51. [52]

    The science of interaction,

    W. A. Pike, J. Stasko, R. Chang, and T. A. O’Connell, “The science of interaction,”Information Visualization, vol. 8, no. 4, pp. 263–274, Dec. 2009

    2009

  52. [53]

    A framework of interaction costs in information visual- ization,

    H. Lam, “A framework of interaction costs in information visual- ization,”IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 6, pp. 1149–1156, 2008

    2008

  53. [54]

    Wilkinson,The Grammar of Graphics, second edition ed., ser

    L. Wilkinson,The Grammar of Graphics, second edition ed., ser. Statistics and computing. Springer, 2005

    2005

  54. [55]

    D3: Data-driven docu- ments,

    M. Bostock, V . Ogievetsky, and J. Heer, “D3: Data-driven docu- ments,”IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 6, pp. 2301–2309, 2011

    2011

  55. [56]

    Vega-Lite: A grammar of interactive graphics,

    A. Satyanarayan, D. Moritz, K. Wongsuphasawat, and J. Heer, “Vega-Lite: A grammar of interactive graphics,”IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 341–350, 2017

    2017

  56. [57]

    The sandbox for analysis: Concepts and evaluation,

    W. Wright, D. Schroh, P . Proulx, A. Skaburskis, and B. Cort, “The sandbox for analysis: Concepts and evaluation,” inProceedings of the ACM Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2006, pp. 801–810

    2006

  57. [58]

    Space to think: Large, high- resolution displays for sensemaking,

    C. Andrews, A. Endert, and C. North, “Space to think: Large, high- resolution displays for sensemaking,” inProceedings of the ACM Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2010, pp. 55–64

    2010

  58. [59]

    Information-rich virtual environments: theory, tools, and research agenda,

    D. A. Bowman, C. North, J. Chen, N. F. Polys, P . S. Pyla, and U. Yilmaz, “Information-rich virtual environments: theory, tools, and research agenda,” inProceedings of the ACM Symposium on Virtual Reality Software and Technology. New York, NY, USA: ACM, 2003, pp. 81–90

    2003

  59. [60]

    Proxemic interactions: the new ubicomp?

    S. Greenberg, N. Marquardt, T. Ballendat, R. Diaz-Marino, and M. Wang, “Proxemic interactions: the new ubicomp?”Interactions, vol. 18, no. 1, pp. 42–50, 2011

    2011

  60. [61]

    E. T. Hall,The Hidden Dimension. Garden City, NY, USA: Anchor Books, 1966

    1966

  61. [62]

    Situated analytics,

    B. H. Thomas, G. F. Welch, P . Dragicevic, N. Elmqvist, P . Irani, Y. Jansen, D. Schmalstieg, A. Tabard, N. A. M. ElSayed, R. T. Smith, and W. Willett, “Situated analytics,” inImmersive Analytics, ser. Lecture Notes in Computer Science, vol. 11190. Berlin, Germany: Springer, 2018, pp. 185–220

    2018

  62. [63]

    DXR: A toolkit for building immersive data visualizations,

    R. Sicat, J. Li, J. Choi, M. Cordeil, W.-K. Jeong, B. Bach, and H. Pfis- ter, “DXR: A toolkit for building immersive data visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 715–725, 2019. 14

    2019

  63. [64]

    IATK: An immersive analytics toolkit,

    M. Cordeil, A. Cunningham, B. Bach, C. Hurter, B. H. Thomas, K. Marriott, and T. Dwyer, “IATK: An immersive analytics toolkit,” inProceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces. Piscataway, NJ, USA: IEEE, 2019, pp. 200–209

    2019

  64. [65]

    VRIA: A web- based framework for creating immersive analytics experiences,

    P . W. S. Butcher, N. W. John, and P . D. Ritsos, “VRIA: A web- based framework for creating immersive analytics experiences,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 7, pp. 3213–3225, 2021

    2021

  65. [66]

    Munin: A peer-to-peer middleware for ubiquitous analytics and visualization spaces,

    S. K. Badam, E. Fisher, and N. Elmqvist, “Munin: A peer-to-peer middleware for ubiquitous analytics and visualization spaces,” IEEE Transactions on Visualization and Computer Graphics, vol. 21, no. 2, pp. 215–228, 2015

    2015

  66. [67]

    Vistrates: A component model for ubiquitous an- alytics,

    S. K. Badam, A. Mathisen, R. R ¨adle, C. N. Klokmose, and N. Elmqvist, “Vistrates: A component model for ubiquitous an- alytics,”IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 586–596, Jan. 2019

    2019

  67. [68]

    Supporting vi- sual exploration for multiple users in large display environments,

    S. K. Badam, F. Amini, N. Elmqvist, and P . Irani, “Supporting vi- sual exploration for multiple users in large display environments,” inProceedings of the IEEE Conference on Visual Analytics Science and Technology. Los Alamitos, CA, USA: IEEE Computer Society, 2016, pp. 1–10

    2016

  68. [69]

    Sensemaking strategies with immersive space to think,

    L. Lisle, K. Davidson, J. K. E. Gitre, C. North, and D. A. Bow- man, “Sensemaking strategies with immersive space to think,” in Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces. Los Alamitos, CA, USA: IEEE Computer Society, 2021, pp. 529–537

    2021

  69. [70]

    Exploring the evolution of sensemaking strategies in immersive space to think,

    K. Davidson, L. Lisle, K. Whitley, D. A. Bowman, and C. North, “Exploring the evolution of sensemaking strategies in immersive space to think,”IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 12, pp. 5294–5307, 2023

    2023

  70. [71]

    DashSpace: A live collaborative platform for immersive and ubiquitous analytics,

    M. Borowski, P . W. S. Butcher, J. B. Kristensen, J. O. Petersen, P . D. Ritsos, C. N. Klokmose, and N. Elmqvist, “DashSpace: A live collaborative platform for immersive and ubiquitous analytics,” IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 10, pp. 7034–7047, 2025

    2025

  71. [72]

    Spatialstrates: Cross-reality collaboration through spatial hypermedia,

    M. Borowski, J. E. S. Grønbæk, P . W. S. Butcher, P . D. Ritsos, C. N. Klokmose, and N. Elmqvist, “Spatialstrates: Cross-reality collaboration through spatial hypermedia,” inProceedings of the ACM Symposium on User Interface Software and Technology. New York, NY, USA: ACM, 2025, pp. 187:1–187:14

    2025

  72. [73]

    Consumed endurance: A metric to quantify arm fatigue of mid- air interactions,

    J. D. Hincapi ´e-Ramos, X. Guo, P . Moghadasian, and P . Irani, “Consumed endurance: A metric to quantify arm fatigue of mid- air interactions,” inProceedings of the ACM Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2014, pp. 1063–1072

    2014

  73. [74]

    Anu.js: Accelerating web-based immersive analytics,

    D. Saffo, B. Lee, F. Lu, C. Y. Wang, and B. MacIntyre, “Anu.js: Accelerating web-based immersive analytics,” inProceedings of the ACM Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2026, pp. 852:1–852:18

    2026

  74. [75]

    ImAxes: Immersive axes as embodied affordances for inter- active multivariate data visualisation,

    M. Cordeil, A. Cunningham, T. Dwyer, B. H. Thomas, and K. Mar- riott, “ImAxes: Immersive axes as embodied affordances for inter- active multivariate data visualisation,” inProceedings of the ACM Symposium on User Interface Software and Technology. New York, NY, USA: ACM, 2017, pp. 71–83

    2017

  75. [76]

    Immersive collaborative analysis of network connectiv- ity: CAVE-style or head-mounted display?

    M. Cordeil, T. Dwyer, K. Klein, B. Laha, K. Marriott, and B. H. Thomas, “Immersive collaborative analysis of network connectiv- ity: CAVE-style or head-mounted display?”IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 441–450, 2017

    2017

  76. [77]

    A survey on cross-virtuality analytics,

    B. Fr ¨ohler, C. Anthes, F. Pointecker, J. Friedl, D. Schwajda, A. Riegler, S. Tripathi, C. Holzmann, M. Brunner, H. Jodlbauer, H. Jetter, and C. Heinzl, “A survey on cross-virtuality analytics,” Computer Graphics Forum, vol. 41, no. 1, pp. 465–494, 2022

    2022

  77. [78]

    Through their eyes and in their shoes: Providing group awareness during collabora- tion across virtual reality and desktop platforms,

    D. Saffo, A. Batch, C. Dunne, and N. Elmqvist, “Through their eyes and in their shoes: Providing group awareness during collabora- tion across virtual reality and desktop platforms,” inProceedings of the ACM Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2023, pp. 383:1–383:15

    2023

  78. [79]

    There is no spoon: Evaluating performance, space use, and presence with expert domain users in immersive analytics,

    A. Batch, A. Cunningham, M. Cordeil, N. Elmqvist, T. Dwyer, B. H. Thomas, and K. Marriott, “There is no spoon: Evaluating performance, space use, and presence with expert domain users in immersive analytics,”IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 536–546, 2020

    2020

  79. [80]

    Supporting team-first visual analytics through group activity representations,

    S. K. Badam, Z. Zeng, E. Wall, A. Endert, and N. Elmqvist, “Supporting team-first visual analytics through group activity representations,” inProceedings of the Graphics Interface Conference. Canadian Human-Computer Communications Society, 2017, pp. 208–213

    2017

  80. [81]

    A multi- literacy model for interactive visualization literacy: Definitions, literacies, and steps for future research,

    G. M. Le ´on, B. Bach, M. Valentim, and N. Elmqvist, “A multi- literacy model for interactive visualization literacy: Definitions, literacies, and steps for future research,” inProceedings of the ACM Conference on Human Factors in Computing Systems. ACM, 2026, pp. 1147:1–1147:16

    2026

Showing first 80 references.