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arxiv: 2606.12648 · v1 · pith:6FNKQTGUnew · submitted 2026-06-10 · 💻 cs.HC

OpenRoundup: Multi-Table Data Wrangling Through Interactive Visualization

Stephen Kasica , Charles Berret , Tamara Munzner This is my paper

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

classification 💻 cs.HC
keywords multi-table data wranglinginteractive visualizationdata journalismtable consolidationeager consolidationStack and Pack operationsbrowser-based tool
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The pith

OpenRoundup lets data journalists consolidate multiple tables into one without code through interactive visual panels and two operations.

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

The paper introduces OpenRoundup to let data journalists merge records from multiple independent sources into one table without writing any code. It does this through an interface of five coordinated panels that show schema previews and data quality alerts while building the result incrementally. Two new ideas drive the system: assembling the combined table early during wrangling and a simple vocabulary of just two operations called Stack and Pack. A replication of seventeen real published workflows shows the approach covers typical journalism tasks. A study with four professional journalists confirms it helps people who grasp the idea of joins but cannot code them, with extra benefits for teaching the topic.

Core claim

The authors present OpenRoundup as a system that treats the collection of tables rather than a single table as the primary unit of work. Users consolidate tables through eager table consolidation, building a composite table early via interactive incremental assembly. This is supported by a declarative vocabulary consisting of the two operations Stack and Pack. The interface runs entirely in the browser with a client-only architecture for privacy, featuring live schema previews, ambient alerts, and a recursive treemap of the operation tree. Evaluation through replication of 17 workflows and deployment with four journalists shows expressive coverage of real-world consolidation tasks and utilit

What carries the argument

Eager table consolidation that assembles a composite table early through interactive incremental assembly of source tables, together with the declarative vocabulary of Stack and Pack operations.

If this is right

  • The system covers the consolidation tasks found in 17 published journalist programming workflows.
  • Practitioners who understand joins conceptually but lack programming skills gain a usable tool for multi-table work.
  • The interface provides secondary value for data journalism education.
  • All processing stays client-side, ensuring privacy for sensitive journalism data.

Where Pith is reading between the lines

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

  • The visual approach could extend to other domains where users combine datasets without coding, such as business reporting or research data integration.
  • The unanticipated education benefit suggests classroom use in data literacy courses beyond journalism.
  • Testing the system with larger collections of tables would reveal whether the current two-operation vocabulary scales to more complex cases.

Load-bearing premise

That reproducing 17 published journalist programming workflows and testing with four professional data journalists is sufficient to establish both expressive coverage of real-world tasks and practical utility for the target user group.

What would settle it

A published journalist workflow from accountability reporting that cannot be completed using only the Stack and Pack operations through the five-panel interface.

Figures

Figures reproduced from arXiv: 2606.12648 by Charles Berret, Stephen Kasica, Tamara Munzner.

Figure 1
Figure 1. Figure 1: OpenRoundup supports interactive table assembly with [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The OpenRoundup interface, showing an intermediate step in the Crime & Heat usage example (Sec. V-A). The [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: OpenRoundup interaction flow. Users navigate clock [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Transient views: alternative tabs and states. The [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: OpenRoundup is built on a three-layer architecture [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: This 3 × 3 matrix defines the nine sagas used to transform data in OpenRoundup. b) State: The State layer maintains a global, semantic representation of application status via five bounded domains, called slices. We divide these into two groups: data slices, which represent the global data space, and UI slices, which represent the global interaction state. The global data space is composed of three slices:… view at source ↗
read the original abstract

Data journalists routinely integrate records across multiple independently published sources to support accountability reporting, yet no existing interactive wrangling tool treats the collection of tables -- rather than the single table -- as its primary unit of work. We present OpenRoundup, an open-source, browser-based system that enables data journalists to consolidate multiple tables into a single analysis-ready output without writing code. The interface comprises five coordinated panels that implement a schema-first, values-on-demand paradigm with live schema previews, ambient data quality alerts, and a recursive treemap visualization of the evolving operation tree. A client-only architecture powered by DuckDB-WASM runs in the browser, providing strong data privacy guarantees suited to sensitive journalism data. The system introduces two conceptual contributions: eager table consolidation, in which a composite table is assembled early in the wrangling phase via interactive, incremental assembly of multiple source tables; and a declarative vocabulary for table consolidation consisting of two operations, Stack and Pack. We evaluate the system through a replication study in which the authors reproduce 17 published journalist programming workflows using only the interface, and a deployment study with four professional data journalists. The replication study demonstrates expressive coverage of real-world consolidation tasks. The deployment study confirms utility for practitioners who understand joins conceptually but lack the programming skills to execute them, and surfaces an unanticipated secondary value for data journalism education.

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 / 2 minor

Summary. The paper presents OpenRoundup, a browser-based interactive visualization system for consolidating multiple tables into a single analysis-ready output without code, targeted at data journalists. It uses five coordinated panels with a schema-first, values-on-demand paradigm, live schema previews, ambient data quality alerts, and a recursive treemap of the operation tree. The client-only DuckDB-WASM architecture provides privacy guarantees. Conceptual contributions include eager table consolidation and a declarative vocabulary of Stack and Pack operations. Evaluation consists of an author-conducted replication study reproducing 17 published journalist programming workflows and a deployment study with four professional data journalists; the paper claims the former demonstrates expressive coverage of real-world tasks and the latter confirms utility for practitioners who understand joins conceptually but lack programming skills, plus secondary value for data journalism education.

Significance. If the evaluations hold with stronger quantitative support, the work would offer a practical advance in interactive multi-table wrangling tools by shifting focus from single-table operations to collection-level consolidation, with notable strengths in open-source release, client-side privacy for sensitive journalism data, and accessibility for non-programmers. The introduction of eager consolidation and the Stack/Pack vocabulary could influence future tool design in HCI and data journalism. Current evidence limitations reduce immediate impact, but the system description and privacy architecture provide a solid foundation for follow-on work.

major comments (2)
  1. [Replication study description] Replication study description (abstract and evaluation section): the claim that the study 'demonstrates expressive coverage of real-world consolidation tasks' is load-bearing for the expressive-coverage contribution, yet the manuscript reports no quantitative metrics such as per-workflow success rates, completion times, error counts, or inter-rater agreement; self-reproduction by authors without independent coders or exclusion criteria leaves the coverage claim without verifiable support.
  2. [Deployment study description] Deployment study description (abstract and evaluation section): the claim that the study 'confirms utility for practitioners who understand joins conceptually but lack the programming skills' rests on n=4 participants with no reported task completion rates, controls for prior tool exposure, task diversity, or exclusion criteria; this sample size and lack of methodology details directly undermine the utility and target-user-group claims.
minor comments (2)
  1. [Introduction / §3] The abstract and introduction introduce 'eager table consolidation' and 'Stack and Pack' without an early formal definition or comparison table to existing join/concatenation operators; a small clarifying table in §3 would improve readability.
  2. [Figures] Figure captions for the treemap and panel coordination diagrams could more explicitly link visual elements to the Stack versus Pack semantics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and detailed feedback on the evaluation sections. The concerns about the strength of evidence in both studies are valid, and we address them point by point below. We propose targeted revisions to qualify claims and expand methodological transparency while preserving the core contributions of the work.

read point-by-point responses

  1. Referee: Replication study description (abstract and evaluation section): the claim that the study 'demonstrates expressive coverage of real-world consolidation tasks' is load-bearing for the expressive-coverage contribution, yet the manuscript reports no quantitative metrics such as per-workflow success rates, completion times, error counts, or inter-rater agreement; self-reproduction by authors without independent coders or exclusion criteria leaves the coverage claim without verifiable support.

    Authors: We agree that the replication study provides no quantitative metrics and was performed by the authors without independent coders or formal exclusion criteria. The study was conceived as a qualitative reproduction exercise to map published journalist workflows onto the system's operations rather than as a controlled experiment. In the revised manuscript we will: (1) expand the evaluation section with a fuller description of workflow selection, the reproduction process, and any difficulties encountered; (2) replace the phrasing 'demonstrates expressive coverage' with the more qualified 'provides evidence of expressive coverage'; and (3) add an explicit limitations paragraph noting the absence of quantitative measures and independent validation. These changes will be made without altering the factual record of the 17 reproductions. revision: partial

  2. Referee: Deployment study description (abstract and evaluation section): the claim that the study 'confirms utility for practitioners who understand joins conceptually but lack the programming skills' rests on n=4 participants with no reported task completion rates, controls for prior tool exposure, task diversity, or exclusion criteria; this sample size and lack of methodology details directly undermine the utility and target-user-group claims.

    Authors: We concur that n=4 is small, that no quantitative completion rates or controls were reported, and that the current text overstates the strength of the evidence. The study was a qualitative deployment with professional data journalists; such small samples are common in HCI work with domain experts but do limit generalizability. In revision we will: (1) supply additional methodological details on participant recruitment, session protocol, and observed usage that were collected but not previously reported; (2) change 'confirms utility' to 'suggests utility' and explicitly state the small sample size as a limitation; and (3) soften the target-user-group claim to reflect that participants already understood joins conceptually. No new data collection is proposed. revision: partial

Circularity Check

0 steps flagged

No circularity: system description and external-task evaluation are self-contained

full rationale

The paper presents a browser-based interface for multi-table wrangling and evaluates it via author replication of 17 published workflows plus a deployment study with four journalists. No equations, fitted parameters, predictions, or self-citations appear in the load-bearing claims. The replication and deployment rest on direct reproduction of external published tasks and user sessions rather than any reduction to the system's own definitions or prior author results. This is the normal non-circular case for an HCI systems paper whose central claims are supported by external artifacts and small-scale user observation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claims rest on the introduction of two new conceptual elements and standard HCI assumptions about what small-scale user studies can demonstrate.

axioms (1)
  • domain assumption Standard HCI assumption that small deployment studies with domain practitioners can indicate practical utility and unanticipated educational value.
    The deployment study with four journalists is used to confirm utility and surface secondary value for education.
invented entities (2)
  • Eager table consolidation no independent evidence
    purpose: Assembling a composite table early via interactive incremental steps
    New conceptual contribution introduced to shift from single-table to multi-table primary unit of work.
  • Stack and Pack operations no independent evidence
    purpose: Declarative vocabulary for table consolidation
    Two operations presented as the core of the new consolidation approach.

pith-pipeline@v0.9.1-grok · 5768 in / 1305 out tokens · 23804 ms · 2026-06-27T08:06:07.935467+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

81 extracted references · 55 canonical work pages

  1. [1]

    Computational Journal- ism,

    S. Cohen, J. T. Hamilton, and F. Turner, “Computational Journal- ism,”Comm. ACM (CACM), vol. 54, no. 10, p. 66, Oct. 2011, 10.1145/2001269.2001288. TVCG JOURNAL SUBMISSION, JUN 2026 17

    work page doi:10.1145/2001269.2001288 2011

  2. [2]

    Meyer,Precision Journalism: A Reporter’s Introduction to Social Science Methods, 4th ed

    P. Meyer,Precision Journalism: A Reporter’s Introduction to Social Science Methods, 4th ed. Rowman & Littlefield Publishers, Inc, 2002

    2002

  3. [3]

    Bounegru and J

    L. Bounegru and J. Gray,The data journalism handbook: Towards a critical data practice. Amsterdam University Press, 2021

    2021

  4. [4]

    Research Directions in Data Wrangling: Visualizations and Transformations for Usable and Credible Data,

    S. Kandel, J. Heer, C. Plaisant, J. Kennedy, F. van Ham, N. Henry Riche, C. Weaver, B. Lee, D. Brodbeck, and P. Puono, “Research Directions in Data Wrangling: Visualizations and Transformations for Usable and Credible Data,”Information Visualization, vol. 10, no. 4, pp. 271–288, 2011, 10.1177/1473871611415994

    work page doi:10.1177/1473871611415994 2011

  5. [5]

    Data Journalism in 2017: The Current State and Challenges Facing the Field Today,

    S. Rogers, J. Schwabish, and D. Bowers, “Data Journalism in 2017: The Current State and Challenges Facing the Field Today,”

    2017

  6. [6]

    Available: https://newslab.withgoogle.com/assets/docs/ data-journalism-in-2017.pdf

    [Online]. Available: https://newslab.withgoogle.com/assets/docs/ data-journalism-in-2017.pdf

    2017

  7. [7]

    The State of Data Journalism Survey,

    European Journalism Centre, “The State of Data Journalism Survey,” 2021. [Online]. Available: https://ejc.net/resources/ data-journalism-survey-2021

    2021

  8. [8]

    Data Journalism Practices Globally: Skills, Education, Opportunities, and Values,

    B. R. Heravi and M. Lorenz, “Data Journalism Practices Globally: Skills, Education, Opportunities, and Values,”Journalism and Media, vol. 1, no. 1, pp. 26–40, 2020, 10.3390/journalmedia1010003

    work page doi:10.3390/journalmedia1010003 2020

  9. [9]

    Table Scraps: An Action- able Framework for Multi-Table Data Wrangling From An Artifact Study of Computational Journalism,

    S. Kasica, C. Berret, and T. Munzner, “Table Scraps: An Action- able Framework for Multi-Table Data Wrangling From An Artifact Study of Computational Journalism,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 27, no. 2, pp. 957–966, 2021, 10.1109/TVCG.2020.3030462

    work page doi:10.1109/tvcg.2020.3030462 2021

  10. [10]

    Data Wrangling for Big Data: Challenges and Opportunities,

    T. Furche, G. Gottlob, L. Libkin, G. Orsi, and N. Paton, “Data Wrangling for Big Data: Challenges and Opportunities,” inProceedings of the 19th International Conference on Extending Database Technology, Bordeaux, France, Mar. 2016, 10.5441/002/EDBT.2016.44

    work page doi:10.5441/002/edbt.2016.44 2016

  11. [11]

    D’Ignazio and L

    C. D’Ignazio and L. F. Klein,Data Feminism. The MIT Press, 2020, 10.7551/mitpress/11805.001.0001

    work page doi:10.7551/mitpress/11805.001.0001 2020

  12. [12]

    Making NLP work for Investigative Journalism,

    J. Stray, “Making NLP work for Investigative Journalism,” Berkeley Institute for Data Science, 2017. [Online]. Available: https://www. youtube.com/watch?v=yRP9DL8E36A

    2017

  13. [13]

    Potter’s Wheel: An Interactive Data Cleaning System,

    V . Raman and J. M. Hellerstein, “Potter’s Wheel: An Interactive Data Cleaning System,” inConf. Very Large Data Bases (VLDB), vol. 27, 2001, pp. 381–390, 10.5555/645927.672045

    work page doi:10.5555/645927.672045 2001

  14. [14]

    Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , pages =

    S. Kandel, A. Paepcke, J. Hellerstein, and J. Heer, “Wrangler: Interactive Visual Specification of Data Transformation Scripts,” inACM Conf. Human Factors in Computing Systems (CHI), 2011, pp. 3363–3372, 10.1145/1978942.1979444

    work page doi:10.1145/1978942.1979444 2011

  15. [15]

    Proactive Wran- gling: Mixed-initiative End-user Programming of Data Transformation Scripts,

    P. J. Guo, S. Kandel, J. M. Hellerstein, and J. Heer, “Proactive Wran- gling: Mixed-initiative End-user Programming of Data Transformation Scripts,” inConf. ACM Symp. User Interface Software and Technology (UIST). ACM, 2011, pp. 65–74, 10.1145/2047196.2047205

    work page doi:10.1145/2047196.2047205 2011

  16. [16]

    Profiler: integrated statistical analysis and visualization for data quality assessment,

    S. Kandel, R. Parikh, A. Paepcke, J. M. Hellerstein, and J. Heer, “Profiler: integrated statistical analysis and visualization for data quality assessment,” inConf. Advanced Visual Interfaces (AVI). Capri Island, Italy: ACM, 2012, pp. 547–554, 10.1145/2254556.2254659

    work page doi:10.1145/2254556.2254659 2012

  17. [17]

    Rigel: Transforming Tabular Data by Declarative Mapping,

    R. Chen, D. Weng, Y . Huang, X. Shu, J. Zhou, G. Sun, and Y . Wu, “Rigel: Transforming Tabular Data by Declarative Mapping,”IEEE Trans. Visualization & Computer Graphics (TVCG), pp. 1–11, 2022, 10.1109/TVCG.2022.3209385

    work page doi:10.1109/tvcg.2022.3209385 2022

  18. [18]

    Buckaroo: A Direct Manipulation Visual Data Wrangler,

    A. Warner, A. McNutt, P. Rosen, and E. K. Rezig, “Buckaroo: A Direct Manipulation Visual Data Wrangler,”Proc. VLDB Endow., vol. 18, no. 12, pp. 5423–5426, 2025, 10.14778/3750601.3750687

    work page doi:10.14778/3750601.3750687 2025

  19. [19]

    Xavier: Toward Better Coding Assistance in Authoring Tabular Data Wrangling Scripts,

    Y . Zhou, X. Cai, Q. Shi, Y . Huang, H. Li, H. Qu, D. Weng, and Y . Wu, “Xavier: Toward Better Coding Assistance in Authoring Tabular Data Wrangling Scripts,” inACM Conf. Human Factors in Computing Systems (CHI), Yokohama Japan, 2025, pp. 1–16, 10.1145/3706598.3714239

    work page doi:10.1145/3706598.3714239 2025

  20. [20]

    Unravel: A Fluent Code Explorer for Data Wrangling,

    N. Shrestha, T. Barik, and C. Parnin, “Unravel: A Fluent Code Explorer for Data Wrangling,” inConf. ACM Symp. User Interface Software and Technology (UIST), 2021, pp. 198–207, 10.1145/3472749.3474744

    work page doi:10.1145/3472749.3474744 2021

  21. [21]

    TACO: Visualizing Changes in Tables Over Time,

    C. Niederer, H. Stitz, R. Hourieh, F. Grassinger, W. Aigner, and M. Streit, “TACO: Visualizing Changes in Tables Over Time,”IEEE Transactions on Visualization and Computer Graphics (InfoVis ’17), vol. 24, no. 1, pp. 677–686, 2018

    2018

  22. [22]

    Effective Use of Data,

    N. Mulvad, “Effective Use of Data,” 2018. [Online]. Available: https://www.youtube.com/watch?v=kcE81ekvvHk

    2018

  23. [23]

    OpenRefine 2024 User Survey Results,

    M. Magdinier, “OpenRefine 2024 User Survey Results,” OpenRefine,

    2024

  24. [24]

    Available: https://openrefine.org/blog/2024/12/20/ 2024-survey-results

    [Online]. Available: https://openrefine.org/blog/2024/12/20/ 2024-survey-results

    2024

  25. [25]

    Tabula: a tool for liberating data tables trapped inside pdf files,

    M. Aristar ´an, M. Tigas, and J. B. Merrill, “Tabula: a tool for liberating data tables trapped inside pdf files,” https://github.com/tabulapdf/tabula, TabulaPDF, 2018

    2018

  26. [26]

    Groskopf, J

    C. Groskopf, J. Germuska, A. Bycoffe, and T. Mehlinger, “csvkit,” Wireservice, 2012. [Online]. Available: https://github.com/wireservice/ csvkit

    2012

  27. [27]

    Groskopf, “Agate,” 2018

    C. Groskopf, “Agate,” 2018. [Online]. Available: https://agate. readthedocs.io

    2018

  28. [28]

    Data integration: the teenage years,

    A. Halevy, A. Rajaraman, and J. Ordille, “Data integration: the teenage years,” inProceedings of the 32nd International Conference on Very Large Data Bases, ser. VLDB ’06. VLDB Endowment, 2006, p. 9–16

    2006

  29. [29]

    Data integration: the current status and the way forward,

    M. Stonebraker and I. F. Ilyas, “Data integration: the current status and the way forward,”IEEE Data Eng. Bull., vol. 41, pp. 3–9, 2018

    2018

  30. [30]

    Smith-Miles

    J. Bleiholder and F. Naumann, “Data fusion,”ACM Comput. Surv., vol. 41, no. 1, pp. 1–41, Jan. 2009, 10.1145/1456650.1456651

    work page doi:10.1145/1456650.1456651 2009

  31. [31]

    Bernstein

    E. Rahm and P. A. Bernstein, “A survey of approaches to automatic schema matching,”The VLDB Journal, vol. 10, no. 4, pp. 334–350, 2001, 10.1007/s007780100057

    work page doi:10.1007/s007780100057 2001

  32. [32]

    Clio: Schema Mapping Creation and Data Exchange,

    R. Fagin, L. M. Haas, M. Hern ´andez, R. J. Miller, L. Popa, and Y . Velegrakis, “Clio: Schema Mapping Creation and Data Exchange,” inConceptual Modeling: Foundations and Applications, A. T. Borgida, V . K. Chaudhri, P. Giorgini, and E. S. Yu, Eds. Springer, 2009, vol. 5600, pp. 198–236, 10.1007/978-3-642-02463-4 12

    work page doi:10.1007/978-3-642-02463-4 2009

  33. [33]

    Interactive generation of integrated schemas,

    L. Chiticariu, P. G. Kolaitis, and L. Popa, “Interactive generation of integrated schemas,” inProceedings of the 2008 ACM SIG- MOD international conference on Management of data, ser. SIGMOD ’08. Association for Computing Machinery, Jun. 2008, pp. 833–846, 10.1145/1376616.1376700

    work page doi:10.1145/1376616.1376700 2008

  34. [34]

    Visualiza- tion of mappings between schemas,

    G. G. Robertson, M. P. Czerwinski, and J. E. Churchill, “Visualiza- tion of mappings between schemas,” inACM Conf. Human Factors in Computing Systems (CHI), Portland, Oregon, USA, 2005, p. 431, 10.1145/1054972.1055032

    work page doi:10.1145/1054972.1055032 2005

  35. [35]

    Sample-driven schema mapping,

    L. Qian, M. J. Cafarella, and H. V . Jagadish, “Sample-driven schema mapping,” inConf. ACM Intl. Conf. on Management of Data (SIGMOD), Scottsdale Arizona USA, May 2012, pp. 73–84, 10.1145/2213836.2213846

    work page doi:10.1145/2213836.2213846 2012

  36. [36]

    Managing data lakes in big data era: What’s a data lake and why has it became popular in data management ecosystem,

    H. Fang, “Managing data lakes in big data era: What’s a data lake and why has it became popular in data management ecosystem,” inProc. IEEE Intl. Conf. Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015, pp. 820–824, 10.1109/CY- BER.2015.7288049

    work page doi:10.1109/cy- 2015

  37. [37]

    Preliminary Guidelines for Combining Data Integration and Visual Data Analysis,

    A. Coscia, A. Suh, R. Chang, and A. Endert, “Preliminary Guidelines for Combining Data Integration and Visual Data Analysis,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 30, no. 10, pp. 6678– 6690, 2024, 10.1109/TVCG.2023.3334513

    work page doi:10.1109/tvcg.2023.3334513 2024

  38. [38]

    Dy- namic workload driven data integration in Tableau,

    K. Morton, R. Bunker, J. Mackinlay, R. Morton, and C. Stolte, “Dy- namic workload driven data integration in Tableau,” inConf. ACM Intl. Conf. on Management of Data (SIGMOD), 2012, p. 807–816, 10.1145/2213836.2213961

    work page doi:10.1145/2213836.2213961 2012

  39. [39]

    Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science,

    S. Kasica, C. Berret, and T. Munzner, “Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science,” inACM Conf. Human Factors in Computing Systems (CHI), New York, NY , USA, 2023, 10.1145/3544548.3581271

    work page doi:10.1145/3544548.3581271 2023

  40. [40]

    Building Mashups by Example,

    R. Tuchinda, P. Szekely, and C. A. Knoblock, “Building Mashups by Example,” inACM Conf. Intelligent User Interfaces (IUI), 2008, pp. 139–149, 10.1145/1378773.1378792

    work page doi:10.1145/1378773.1378792 2008

  41. [41]

    End-User Programming of Mashups with Vegemite,

    J. Lin, J. Wong, J. Nichols, A. Cypher, and T. A. Lau, “End-User Programming of Mashups with Vegemite,” inACM Conf. Intelligent User Interfaces (IUI). New York, NY , USA: ACM, 2009, pp. 97–106, 10.1145/1502650.1502667

    work page doi:10.1145/1502650.1502667 2009

  42. [42]

    Interactive Data Integration through Smart Copy & Paste,

    Z. Ives, C. Knoblock, S. Minton, M. Jacob, P. Talukdar, R. Tuchinda, J. L. Ambite, M. Muslea, and C. Gazen, “Interactive Data Integration through Smart Copy & Paste,”Conf. Innovative Data Systems Research (CIDR), 2009

    2009

  43. [43]

    The Emerging Web of Linked Data,

    C. Bizer, “The Emerging Web of Linked Data,”IEEE Intell. Syst., vol. 24, no. 5, pp. 87–92, 2009, 10.1109/MIS.2009.102

    work page doi:10.1109/mis.2009.102 2009

  44. [44]

    Potluck: Semi-ontology Alignment for Casual Users,

    D. F. Huynh, R. C. Miller, and D. R. Karger, “Potluck: Semi-ontology Alignment for Casual Users,” inThe Semantic Web, ser. Lecture Notes in Computer Science, K. Aberer, K.-S. Choi, N. Noy, D. Allemang, K.-I. Lee, L. Nixon, J. Golbeck, P. Mika, D. Maynard, R. Mizoguchi, G. Schreiber, and P. Cudr´e-Mauroux, Eds. Berlin, Heidelberg: Springer, 2007, pp. 903–9...

    work page doi:10.1007/978-3-540-76298-0 2007

  45. [45]

    Carp´e data: supporting serendipitous data integration in personal information management,

    M. Van Kleek, D. A. Smith, H. S. Packer, J. Skinner, and N. R. Shad- bolt, “Carp´e data: supporting serendipitous data integration in personal information management,” inProceedings of the SIGCHI Conference on Human Factors in Computing Systems. Paris France: ACM, Apr. 2013, pp. 2339–2348, 10.1145/2470654.2481324

    work page doi:10.1145/2470654.2481324 2013

  46. [46]

    CA V A: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs,

    D. Cashman, S. Xu, S. Das, F. Heimerl, C. Liu, S. R. Humayoun, M. Gleicher, A. Endert, and R. Chang, “CA V A: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 27, no. 2, pp. 1731–1741, 2021, 10.1109/TVCG.2020.3030443. TVCG JOURNAL SUBMISSION, JUN 2026 18

    work page doi:10.1109/tvcg.2020.3030443 2021

  47. [47]

    KTabulator: Inter- active Ad hoc Table Creation using Knowledge Graphs,

    S. Xia, N. Anzum, S. Salihoglu, and J. Zhao, “KTabulator: Inter- active Ad hoc Table Creation using Knowledge Graphs,” inACM Conf. Human Factors in Computing Systems (CHI), 2021, pp. 1–14, 10.1145/3411764.3445227

    work page doi:10.1145/3411764.3445227 2021

  48. [48]

    OpenRefine,

    D. Huynh and S. Mazzocchi, “OpenRefine,” 2012. [Online]. Available: openrefine.org

    2012

  49. [49]

    Uncovering data landscapes through data reconnaissance and task wrangling,

    A. Crisan and T. Munzner, “Uncovering data landscapes through data reconnaissance and task wrangling,” inIEEE Conf. Visualization and Vi- sual Analytics (VIS), 2019, pp. 46–50, 10.1109/VISUAL.2019.8933542

    work page doi:10.1109/visual.2019.8933542 2019

  50. [50]

    Data Integration for Heterogenous Datasets,

    J. Hendler, “Data Integration for Heterogenous Datasets,”Big Data, vol. 2, no. 4, pp. 205–215, 2014, 10.1089/big.2014.0068

    work page doi:10.1089/big.2014.0068 2014

  51. [51]

    Where Do Stories Come From? Examining the Exploration Process in Investigative Data Journalism,

    D. Showkat and E. P. Baumer, “Where Do Stories Come From? Examining the Exploration Process in Investigative Data Journalism,” ACM Human-Computer Interaction (PACM-CSCW), vol. 5, pp. 1–31, 2021, 10.1145/3479534

    work page doi:10.1145/3479534 2021

  52. [52]

    Iceberg sensemaking: A process model for critical data analysis and visualization,

    C. Berret and T. Munzner, “Iceberg sensemaking: A process model for critical data analysis and visualization,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 31, no. 9, pp. 6067–6084, 2025, 10.1109/TVCG.2024.3486613

    work page doi:10.1109/tvcg.2024.3486613 2025

  53. [53]

    Building Highly-Coordinated Visualizations in Improvise,

    C. Weaver, “Building Highly-Coordinated Visualizations in Improvise,” inIEEE Symp. Information Visualization (InfoVis), 2004, pp. 159–166, 10.1109/INFVIS.2004.12

    work page doi:10.1109/infvis.2004.12 2004

  54. [54]

    The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,

    B. Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” inIEEE Symp. Visual Languages (VL), 1996, p. 336, 10.1109/VL.1996.545307

    work page doi:10.1109/vl.1996.545307 1996

  55. [55]

    D. A. Norman,The design of everyday things. New York, NY , USA: Basic Books, 2013

    2013

  56. [56]

    Shneiderman, C

    B. Shneiderman, C. Plaisant, M. Cohen, S. Jacobs, N. Elmqvist, and N. Diakopoulos,Designing the User Interface: Strategies for Effective Human-Computer Interaction, 6th ed. Pearson, 2016

    2016

  57. [57]

    Exploring the Placement and Design of Word-Scale Visualizations,

    P. Goffin, W. Willett, J.-D. Fekete, and P. Isenberg, “Exploring the Placement and Design of Word-Scale Visualizations,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 20, no. 12, pp. 2291– 2300, 2014, 10.1109/TVCG.2014.2346435

    work page doi:10.1109/tvcg.2014.2346435 2014

  58. [58]

    Direct manipulation: A step beyond programming languages,

    B. Shneiderman, “Direct manipulation: A step beyond programming languages,”IEEE Computer, vol. 16, no. 08, pp. 57–69, 1983, 10.1109/MC.1983.1654471

    work page doi:10.1109/mc.1983.1654471 1983

  59. [59]

    Ware,Information visualization : perception for design, 2nd ed

    C. Ware,Information visualization : perception for design, 2nd ed. San Francisco ; Tokyo: Morgan Kaufmann Publishers, 2004

    2004

  60. [60]

    Enterprise Data Analysis and Visualization: An Interview Study,

    S. Kandel, A. Paepcke, J. M. Hellerstein, and J. Heer, “Enterprise Data Analysis and Visualization: An Interview Study,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 18, no. 12, pp. 2917– 2926, 2012, 10.1109/TVCG.2012.219

    work page doi:10.1109/tvcg.2012.219 2012

  61. [61]

    E. R. Tufte,Envisioning Information. Cheshire, CT, USA: Graphics Press, 1990

    1990

  62. [62]

    Visual comparison for information visualiza- tion,

    M. Gleicher, D. Albers, R. Walker, I. Jusufi, C. D. Hansen, and J. C. Roberts, “Visual comparison for information visualiza- tion,”Information Visualization, vol. 10, no. 4, pp. 289–309, 2011, 10.1177/1473871611416549

    work page doi:10.1177/1473871611416549 2011

  63. [63]

    Untidy Data: The Unreasonable Effectiveness of Tables,

    L. Bartram, M. Correll, and M. Tory, “Untidy Data: The Unreasonable Effectiveness of Tables,”IEEE Trans. Visualization & Computer Graph- ics (TVCG), vol. 28, pp. 686–696, 2022, 10.1109/TVCG.2021.3114830

    work page doi:10.1109/tvcg.2021.3114830 2022

  64. [64]

    Interactive browsing and navigation in relational databases,

    M. Kahng, S. B. Navathe, J. T. Stasko, and D. H. P. Chau, “Interactive browsing and navigation in relational databases,”Proc. VLDB Endow. (VLDB), vol. 9, no. 12, pp. 1017–1028, 2016, 10.14778/2994509.2994520

    work page doi:10.14778/2994509.2994520 2016

  65. [65]

    Dynamic queries for information exploration: an implementation and evaluation,

    C. Ahlberg, C. Williamson, and B. Shneiderman, “Dynamic queries for information exploration: an implementation and evaluation,” inACM Conf. Human Factors in Computing Systems (CHI), 1992, pp. 619–626, 10.1145/142750.143054

    work page doi:10.1145/142750.143054 1992

  66. [66]

    The Shaping of Information by Visual Metaphors,

    C. Ziemkiewicz and R. Kosara, “The Shaping of Information by Visual Metaphors,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 14, no. 6, pp. 1269–1276, 2008, 10.1109/TVCG.2008.171

    work page doi:10.1109/tvcg.2008.171 2008

  67. [67]

    Arcs, angles, or areas: Individual data encodings in pie and donut charts,

    D. Skau and R. Kosara, “Arcs, angles, or areas: Individual data encodings in pie and donut charts,”Computer Graphics Forum, vol. 35, no. 3, pp. 121–130, 2016, doi.org/10.1111/cgf.12888

    work page doi:10.1111/cgf.12888 2016

  68. [68]

    Looks Good To Me: Visualizations As Sanity Checks,

    M. Correll, M. Li, G. Kindlmann, and C. Scheidegger, “Looks Good To Me: Visualizations As Sanity Checks,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 25, no. 1, pp. 830–839, 2019, 10.1109/TVCG.2018.2864907

    work page doi:10.1109/tvcg.2018.2864907 2019

  69. [69]

    B. W. Silverman,Density Estimation for Statistics and Data Analysis. London: Chapman and Hall, 1986

    1986

  70. [70]

    UpSet: Visualization of Intersecting Sets,

    A. Lex, N. Gehlenborg, H. Strobelt, R. Vuillemot, and H. Pfister, “UpSet: Visualization of Intersecting Sets,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 20, no. 12, pp. 1983–1992, 2014, 10.1109/TVCG.2014.2346248

    work page doi:10.1109/tvcg.2014.2346248 1983

  71. [71]

    Keeping multiple views consistent: Constraints, validations, and exceptions in visualization authoring,

    Z. Qu and J. Hullman, “Keeping multiple views consistent: Constraints, validations, and exceptions in visualization authoring,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 24, no. 1, pp. 468– 477, 2018, 10.1109/TVCG.2017.2744198

    work page doi:10.1109/tvcg.2017.2744198 2018

  72. [72]

    Snap-together visualization: a user interface for coordinating visualizations via relational schemata,

    C. North and B. Shneiderman, “Snap-together visualization: a user interface for coordinating visualizations via relational schemata,” inConf. Advanced Visual Interfaces (AVI), 2000, pp. 128–135, 10.1145/345513.345282

    work page doi:10.1145/345513.345282 2000

  73. [73]

    Vega- Lite: A grammar of interactive graphics,

    A. Satyanarayan, D. Moritz, K. Wongsuphasawat, and J. Heer, “Vega- Lite: A grammar of interactive graphics,”IEEE Trans. Visualization & Computer Graphics (TVCG), vol. 23, no. 1, p. 341–350, 2017, 10.1109/TVCG.2016.2599030

    work page doi:10.1109/tvcg.2016.2599030 2017

  74. [74]

    DuckDB-W ASM: fast analytical processing for the web,

    A. Kohn, D. Moritz, M. Raasveldt, H. M ¨uhleisen, and T. Neu- mann, “DuckDB-W ASM: fast analytical processing for the web,” Proc. VLDB Endow. (VLDB), vol. 15, no. 12, p. 3574–3577, 2022, 10.14778/3554821.3554847

    work page doi:10.14778/3554821.3554847 2022

  75. [75]

    Garcia-Molina and K

    H. Garcia-Molina and K. Salem, “Sagas,”ACM Sigmod Record, vol. 16, no. 3, pp. 249–259, 1987, 10.1145/38714.38742

    work page doi:10.1145/38714.38742 1987

  76. [76]

    Di ´ataxis: A systematic framework for technical documentation authoring,

    D. Procida, “Di ´ataxis: A systematic framework for technical documentation authoring,” 2023, accessed: 2026-05-28. [Online]. Available: https://diataxis.fr

    2023

  77. [77]

    Ask IRE: What is a data diary, and why should I use one?

    D. Malan, “Ask IRE: What is a data diary, and why should I use one?” The Investigative Reporters & Editors Journal, vol. 41, no. 4, p. 3, 2018

    2018

  78. [78]

    Ten Simple Rules for Reproducible Computational Research,

    G. K. Sandve, A. Nekrutenko, J. Taylor, and E. Hovig, “Ten Simple Rules for Reproducible Computational Research,”PLoS Comput Biol, vol. 9, no. 10, p. e1003285, 2013, 10.1371/journal.pcbi.1003285

    work page doi:10.1371/journal.pcbi.1003285 2013

  79. [79]

    Tidy Data,

    H. Wickham, “Tidy Data,”Journal of Statistical Software, vol. 59, no. 1, pp. 1–23, 2014, 10.18637/jss.v059.i10

    work page doi:10.18637/jss.v059.i10 2014

  80. [80]

    Codd,The Relational Model for Database Management, 2nd ed

    E. Codd,The Relational Model for Database Management, 2nd ed. Boston, MA, USA: Addison-Wesley Longman Publishing, 1990

    1990

Showing first 80 references.