Human-Data Interaction, Exploration & Visualization in the AI Era

Challenges & Opportunities for Human-centered Systems

Jean-Daniel Fekete Yifan Hu Dominik Moritz Arnab Nandi Senjuti Basu Roy Eugene Wu Nikos Bikakis George Papastefanatos Panos K. Chrysanthis Guoliang Li Lingyun Yu

University Paris-Saclay & Inria · Northeastern University · Carnegie Mellon University & Apple · Ohio State University · New Jersey Inst. of Technology · Columbia University · Hellenic Mediterranean University & Archimedes/Athena RC · Athena RC · University of Pittsburgh · Tsinghua University · Xi'an Jiaotong-Liverpool University

Abstract

The rapid advancement of AI is transforming human-centered systems, with major implications for human-AI interaction, human-data interaction, and visual analytics. Data analysis increasingly involves large-scale, heterogeneous, and multimodal data that is often unstructured, while foundation models such as LLMs and VLMs introduce new uncertainty into analytical workflows.

The paper examines how these changes expose persistent challenges in latency, scalability, interaction, exploration, reliability, and interpretability. It argues for end-to-end, human-centered systems that co-design data management, AI components, interfaces, and visualization, while keeping humans meaningfully involved through feedback, verification, and trust calibration.

Key Insights

Tightly coupled requirements

Human-data interaction systems must integrate responsive interaction, multimodal data handling, guidance, visualization, and trustworthy AI feedback.

Latency and scalability are barriers

Multimodal data, complex interactions, embeddings, streams, and deep learning models all intensify scalability and responsiveness constraints.

End-to-end design is essential

System architecture, data operations, AI models, interfaces, and visualization need to be designed together, not optimized independently.

AI requires human oversight

AI can support guidance, semantic understanding, and usability, but brittleness, errors, unpredictability, and bias make human-in-the-loop oversight indispensable.

Visualization becomes generative

Visualization is shifting from static depiction to progressive, narrative, aesthetics-aware, and adaptive guidance for sensemaking and trust.

Cross-disciplinary work is crucial

The research agenda spans systems, databases, AI, information visualization, HCI, computer graphics, and cognitive science.

Challenges & Opportunities

Latency issues

Interactive systems need real-time response to preserve analytical flow.

Human-centered data management

Query processing, indexing, and storage can be optimized for interactive analytics.

Scalability

Large datasets and high-velocity analysis require scalable data operations.

Guided exploration

AI suggestions can help users discover what can be asked and analyzed.

AI uncertainty

Errors and bias require verification, provenance, and human oversight.

AR/VR interfaces

Virtual and augmented environments can support in-the-world analytics.

Multimodal complexity

Text, images, audio, video, and rich documents require new abstractions.

Narrative visualization

Data storytelling can guide attention and improve interpretation.

Further Reading

For further details on the discussed topics, readers are pointed to the following review papers and overview works. Click each topic tile to expand the corresponding publications.

Human–AI collaboration

Human-centered AI, interactive AI, data storytelling, and human-AI interaction.

Click to view publications

Human-centered data management

Systems, benchmarks, exploration, visualization-aware processing, scalability, and interactive analytics foundations.

Click to view publications

Visual analytics

Grand challenges, insight mining, design handoff, and human-centered visual analytics methods.

Click to view publications

AI and visual analytics

Generative AI, AI4VIS, ML4VIS, foundation models, evaluation benchmarks, and visual analytics for machine learning.

Click to view publications

References

Show extended reference list 141 items
  1. B. Shneiderman, Human-Centered AI, Oxford University Press, 2022.
  2. M. Raees, I. Meijerink, I. Lykourentzou, V. Khan, K. Papangelis, From Explainable to Interactive AI: a Literature Review on Current Trends in Human-AI Interaction, Int. J. Hum. Comput. Stud. (2024).
  3. H. Li, Y. Wang, H. Qu, Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration, in: ACM CHI, 2024.
  4. S. Amershi, D. S. Weld, M. Vorvoreanu, A. Fourney, B. Nushi, P. Collisson, J. Suh, S. T. Iqbal, P. N. Bennett, K. Inkpen, J. Teevan, R. Kikin-Gil, E. Horvitz, Guidelines for Human-AI Interaction, in: ACM CHI, 2019.
  5. E. Wu, Y. Chen, H. Mohammed, Z. Huang, A Decade of Systems for Human Data Interaction (2025). arXiv:2511.15585.
  6. S. Amer-Yahia, L. Battle, Y. Hu, D. Moritz, A. Parameswaran, N. Bikakis, P. K. Chrysanthis, G. Li, G. Papastefanatos, L. Yu, Data Exploration and Visual Analytics Challenges in AI Era, ACM SIGMOD Blog (Jan. 2025). URL https://wp.sigmod.org/?p=3820
  7. N. Bikakis, P. K. Chrysanthis, G. Li, G. Papastefanatos, L. Yu, Visual Analytics Challenges and Trends in the Age of AI: The BigVis Community Perspective, ACM SIGMOD Record (2025).
  8. L. Battle, C. Scheidegger, A Structured Review of Data Management Technology for Interactive Visualization and Analysis, IEEE TVCG 27 (2) (2021).
  9. G. Richer, A. Pister, M. Abdelaal, J. Fekete, M. Sedlmair, D. Weiskopf, Scalability in Visualization, IEEE TVCG 30 (7) (2024).
  10. A. Ulmer, M. Angelini, J. Fekete, J. Kohlhammer, T. May, A Survey on Progressive Visualization, IEEE TVCG 30 (9) (2024).
  11. X. Qin, Y. Luo, N. Tang, G. Li, Making Data Visualization More Efficient and Effective: a Survey, VLDBJ 29 (1) (2020).
  12. E. Wu, F. Psallidas, Z. Miao, H. Zhang, L. Rettig, Combining Design and Performance in a Data Visualization Management System, in: CIDR, 2017.
  13. N. Bikakis, Big Data Visualization Tools, in: Encyclopedia of Big Data Technologies, 2nd Ed., Springer, 2022.
  14. G. Andrienko, N. Andrienko, S. Drucker, J.-D. Fekete, D. Fisher, S. Idreos, T. Kraska, G. Li, K.-L. Ma, J. D. Mackinlay, A. Oulasvirta, T. Schreck, H. Schumann, M. Stonebraker, D. Auber, N. Bikakis, P. K. Chrysanthis, G. Papastefanatos, M. Sharaf, Big Data Visualization and Analytics: Future Research Challenges and Emerging Applications, in: Workshop on Big Data Visual Exploration and Analytics (BigVis 2020), 2020.
  15. L. Jiang, P. Rahman, A. Nandi, Evaluating Interactive Data Systems: Workloads, Metrics, and Guidelines, in: ACM SIGMOD, 2018.
  16. L. Battle, P. Eichmann, M. Angelini, T. Catarci, G. Santucci, Y. Zheng, C. Binnig, J. Fekete, D. Moritz, Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data, in: ACM SIGMOD, 2020.
  17. P. Eichmann, E. Zgraggen, C. Binnig, T. Kraska, IBEDench: A Benchmark for Interactive Data Exploration, in: ACM SIGMOD, 2020.
  18. A. Wu, D. Deng, M. Chen, S. Liu, D. A. Keim, R. Maciejewski, S. Miksch, H. Strobelt, F. B. Viégas, M. Wattenberg, Grand Challenges in Visual Analytics Applications, CG&A (2023).
  19. Y. Lian, J. Hao, W. Zeng, Q. Luo, A Survey of Visual Insight Mining: Connecting Data and Insights via Visualization, Visual Informatics (2025).
  20. J. Walny, C. Frisson, M. West, D. Kosminsky, S. Knudsen, S. Carpendale, W. Willett, Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff, IEEE TVCG 26 (1) (2020).
  21. E. Adamakis, G. Margetis, S. Ntoa, C. Stephanidis, A Methodological Approach Towards Human-Centered Visual Analytics, Visual Informatics (2025).
  22. R. C. Basole, T. Major, Generative AI for Visualization: Opportunities and Challenges, IEEE TVCG 44 (2) (2024).
  23. J. Wang, S. Liu, W. Zhang, Visual Analytics for Machine Learning: A Data Perspective Survey, IEEE TVCG 30 (12) (2024).
  24. A. Wu, Y. Wang, X. Shu, D. Moritz, W. Cui, H. Zhang, D. Zhang, H. Qu, AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization, IEEE TVCG 28 (12) (2022).
  25. Q. Wang, Z. Chen, Y. Wang, H. Qu, A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization, IEEE TVCG 28 (12) (2022).
  26. N. V. Andrienko, G. L. Andrienko, L. Adilova, S. Wrobel, T. Rhyne, Visual Analytics for Human-Centered Machine Learning, CG&A (2022).
  27. Y. Ye, J. Hao, Y. Hou, Z. Wang, S. Xiao, Y. Luo, W. Zeng, Generative AI for Visualization: State of the Art and Future Directions, Visual Informatics 8 (1) (2024).
  28. W. Yang, M. Liu, Z. Wang, S. Liu, Foundation Models Meet Visualizations: Challenges and Opportunities, Comput. Vis. Media 10 (3) (2024).
  29. N. Elmqvist, C. N. Klokmose, Automating the Path: An R&D Agenda for Human-Centered AI and Visualization, IEEE TVCG 45 (3) (2025).
  30. J. Yuan, C. Chen, W. Yang, M. Liu, J. Xia, S. Liu, A Survey of Visual Analytics Techniques for Machine Learning, Comput. Vis. Media 7 (1) (2021).
  31. F. Hohman, M. Kahng, R. S. Pienta, D. H. Chau, Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers, IEEE TVCG 25 (8) (2019).
  32. N. Chen, Y. Zhang, J. Xu, K. Ren, Y. Yang, VisEval: A Benchmark for Data Visualization in the Era of Large Language Models, IEEE TVCG 31 (1) (2025).
  33. V. Raman, J. M. Hellerstein, Potter’s Wheel: An Interactive Data Cleaning System, in: VLDB, 2001.
  34. D. Alabi, E. Wu, PFunk-H: Approximate Query Processing Using Perceptual Models, in: HILDA, 2016.
  35. F. Li, B. Wu, K. Yi, Z. Zhao, Wander Join and XDB: Online Aggregation via Random Walks, ACM TODS 44 (1) (2019).
  36. B. Ding, S. Huang, S. Chaudhuri, K. Chakrabarti, C. Wang, Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee, in: ACM SIGMOD, 2016.
  37. T. Kraska, Northstar: An Interactive Data Science System, PVLDB 11 (12) (2018).
  38. J. M. Hellerstein, P. J. Haas, H. J. Wang, Online Aggregation, in: ACM SIGMOD, 1997.
  39. D. Moritz, D. Fisher, B. Ding, C. Wang, Trust, but Verify: Optimistic Visualizations of Approximate Queries for Exploring Big Data, in: ACM CHI, 2017.
  40. A. Kim, E. Blais, A. G. Parameswaran, P. Indyk, S. Madden, R. Rubinfeld, Rapid Sampling for Visualizations with Ordering Guarantees, PVLDB (2015).
  41. S. Rahman, M. Aliakbarpour, H. Kong, E. Blais, K. Karahalios, A. G. Parameswaran, R. Rubinfeld, I’ve Seen "Enough": Incrementally Improving Visualizations to Support Rapid Decision Making, PVLDB (2017).
  42. Y. Park, M. J. Cafarella, B. Mozafari, Visualization-aware Sampling for Very Large Databases, in: IEEE ICDE, 2016.
  43. S. Maroulis, N. Bikakis, V. Stamatopoulos, G. Papastefanatos, Adaptive Indexing for Approximate Query Processing in Interactive Data Analysis, arXiv:2505.19872 (2025).
  44. Y. Wang, Y. Wang, X. Chen, Y. Zhao, F. Zhang, E. Wu, C. Fu, X. Yu, OM3: An Ordered Multi-level Min-Max Representation for Interactive Progressive Visualization of Time Series, ACM SIGMOD 1 (2) (2023).
  45. U. Jugel, Z. Jerzak, G. Hackenbroich, V. Markl, M4: A Visualization-Oriented Time Series Data Aggregation, PVLDB 7 (10) (2014).
  46. U. Jugel, Z. Jerzak, G. Hackenbroich, V. Markl, Faster visual analytics through pixel-perfect aggregation, PVLDB 7 (13) (2014).
  47. P. R. Doshi, E. A. Rundensteiner, M. O. Ward, Prefetching for Visual Data Exploration, in: DASFAA, 2003.
  48. B. Shneiderman, Extreme Visualization: Squeezing a Billion Records into a Million Pixels, in: ACM SIGMOD, 2008.
  49. L. Battle, R. Chang, M. Stonebraker, Dynamic Prefetching of Data Tiles for Interactive Visualization, in: ACM SIGMOD, 2016.
  50. H. Mohammed, Z. Wei, R. Netravali, E. Wu, Continuous Prefetch for Interactive Data Applications, PVLDB 13 (11) (2020).
  51. F. Tauheed, T. Heinis, F. Schürmann, H. Markram, A. Ailamaki, SCOUT: Prefetching for Latent Feature Following Queries, PVLDB 5 (11) (2012).
  52. M. A. Khan, A. Nandi, Flux Capacitors for JavaScript Deloreans: Approximate Caching for Physics-Based Data Interaction, in: ACM IUI, 2019.
  53. C. A. de Lara Pahins, S. A. Stephens, C. Scheidegger, J. L. D. Comba, Hashedcubes: Simple, Low Memory, Real-Time Visual Exploration of Big Data, IEEE TVCG 23 (1) (2017).
  54. S. Agarwal, B. Mozafari, A. Panda, H. Milner, S. Madden, I. Stoica, Blinkdb: Queries with Bounded Errors and Bounded Response Times on Very Large Data, in: EuroSys, 2013.
  55. Z. Liu, B. Jiang, J. Heer, imMens: Real-Time Visual Querying of Big Data, Comput. Graph. Forum 32 (3) (2013).
  56. W. Tao, X. Liu, Y. Wang, L. Battle, C. Demiralp, R. Chang, M. Stonebraker, Kyrix: Interactive Pan/Zoom Visualizations at Scale, Comput. Graph. Forum 38 (3) (2019).
  57. D. Moritz, B. Howe, J. Heer, Falcon: Balancing Interactive Latency and Resolution Sensitivity for Scalable Linked Visualizations, in: ACM CHI, 2019.
  58. S. Maroulis, N. Bikakis, G. Papastefanatos, P. Vassiliadis, Y. Vassiliou, Resource-Aware Adaptive Indexing for In-situ Visual Exploration and Analytics, VLDBJ (2022).
  59. Y. Wu, R. Chang, J. M. Hellerstein, A. Satyanarayan, E. Wu, DIEL: Interactive Visualization Beyond the Here and Now, IEEE TVCG (2019).
  60. Y. Wu, R. Chang, J. M. Hellerstein, E. Wu, Facilitating Exploration with Interaction Snapshots under High Latency, IEEE VIS (2018).
  61. A. D. Fekete, I. Gupta, A. G. Parameswaran, F. Chen, C. D. Leon, T. Wattanawaroon, J. Yun, S. Seshadri, Z. Yi, C. Lien, R. Sun, R. Durrani, J. E. Gonzalez, A. D. Joseph, Transactional Panorama: A Conceptual Framework for User Perception in Analytical Visual Interfaces (2023). arXiv:2302.05476.
  62. Y. Chen, X. Li, J. Tao, L. Ramjit, R. Netravali, S. Mitra, A. Parameswaran, J. Ghaderi, D. Rubenstein, E. Wu, Physical Visualization Design: Decoupling Interface and System Design, ACM SIGMOD (2025).
  63. Z. Huang, P. K. Damalapati, E. Wu, Aggregation Consistency Errors in Semantic Layers and How to Avoid Them, in: HILDA, 2023.
  64. J. Hyde, J. Fremlin, Measures in SQL, ACM SIGMOD (2024).
  65. Z. Huang, E. Wu, Lightweight Materialization for Fast Dashboards Over Joins, ACM SIGMOD 1 (2023).
  66. F. Psallidas, E. Wu, Provenance for Interactive Visualizations, in: HILDA, 2018.
  67. F. Psallidas, E. Wu, Demonstration of Smoke: A Deep Breath of Data-Intensive Lineage Applications, in: ACM SIGMOD, 2018.
  68. F. Psallidas, E. Wu, Smoke: Fine-Grained Lineage at Interactive Speed, PVLDB 11 (2018).
  69. H. Mohammed, C. Summers, S. Kaushik, E. Wu, SmokedDuck Demonstration: SQLStepper, ACM SIGMOD (2023).
  70. S. Gathani, P. Lim, L. Battle, Debugging Database Queries: A Survey of Tools, Techniques, and Users, in: ACM CHI, 2020.
  71. S. Roy, L. Orr, D. Suciu, Explaining Query Answers with Explanation-Ready Databases, PVLDB 9 (4) (2015).
  72. Z. Huang, E. Wu, Reptile: Aggregation-Level Explanations for Hierarchical Data, in: ACM SIGMOD, 2022.
  73. W. Wu, L. Flokas, E. Wu, J. Wang, Complaint-Driven Training Data Debugging for Query 2.0, in: ACM SIGMOD, 2020.
  74. E. Wu, S. Madden, Scorpion: Explaining Away Outliers in Aggregate Queries, PVLDB (2013).
  75. F. Abuzaid, P. Kraft, S. Suri, E. Gan, E. Xu, A. Shenoy, A. Ananthanarayan, J. Sheu, E. Meijer, X. Wu, et al., Diff: a Relational Interface for Large-Scale Data Explanation, PVLDB 12 (4) (2018).
  76. H. Mohammed, A. Yao, L. Flokas, C. Summers, H. Zhong, G. Chan, S. Mitra, E. Wu, FaDE: More Than a Million What-ifs per Second, PVLDB (2025).
  77. S. Sarawagi, G. Sathe, i3: Intelligent, Interactive Investigation of Olap Data Cubes, SIGMOD Record (2000).
  78. E. Wu, View Composition Algebra for Ad Hoc Comparisons, IEEE TVCG (2022).
  79. E. Wu, Extending the View Composition Algebra to Hierarchical Data (2022). arXiv:2205.01283.
  80. Y. Chen, E. Wu, PI2: End-to-End Interactive Visualization Interface Generation from Queries, in: ACM SIGMOD, 2021.
  81. H. Zhang, T. Sellam, E. Wu, Mining Precision Interfaces From Query Logs (2017). arXiv:1904.02344.
  82. Y. Chen, J. Tao, E. Wu, DIG: The Data Interface Grammar, in: HILDA, 2023.
  83. E. Wu, R. Chang, Design-Specific Transformations in Visualization, IEEE VIS BELIV Workshop (2024).
  84. E. Wu, X. Y. Tuang, A. Li, V. Bainwala, A Formalism and Library for Database Visualization (2025). arXiv:2504.08979.
  85. E. Wu, Database Theory in Action: Database Visualization, in: ICDT, 2025.
  86. E. F. Codd, A Relational Model of Data for Large Shared Data Banks, Commun. ACM 13 (1970).
  87. D. Zha, Z. P. Bhat, K. Lai, F. Yang, Z. Jiang, S. Zhong, X. Hu, Data-Centric Artificial Intelligence: A Survey, CoRR abs/2303.10158 (2023).
  88. S. E. Whang, Y. Roh, H. Song, J. Lee, Data Collection and Quality Challenges in Deep Learning: a Data-Centric AI Perspective, VLDBJ 32 (4) (2023).
  89. S. Robertson, Z. J. Wang, D. Moritz, M. B. Kery, F. Hohman, Angler: Helping Machine Translation Practitioners Prioritize Model Improvements, in: ACM CHI, 2023.
  90. Á. A. Cabrera, E. Fu, D. Bertucci, K. Holstein, A. Talwalkar, J. I. Hong, A. Perer, Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning, in: ACM CHI, 2023.
  91. H. A. Simon, Designing Organizations for an Information Rich World, in: Computers, Communications, and the Public Interest, Johns Hopkins Press, 1971.
  92. J. W. Tukey, M. B. Wilk, Data Analysis and Statistics: an Expository Overview, in: American Federation of Information Processing Societies (AFIPS), 1966.
  93. J. E. Allen, C. I. G. and Eric Horvitz, Mixed-Initiative Interaction, IEEE Intelligent Systems and their Applications 14 (5) (1999).
  94. K. Wongsuphasawat, D. Moritz, A. Anand, J. D. Mackinlay, B. Howe, J. Heer, Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations, IEEE TVCG 22 (1) (2016).
  95. W. Epperson, V. Gorantla, D. Moritz, A. Perer, Dead or Alive: Continuous Data Profiling for Interactive Data Science, IEEE TVCG 30 (1) (2024).
  96. Z. Liu, J. Heer, The Effects of Interactive Latency on Exploratory Visual Analysis, IEEE TVCG 20 (12) (2014).
  97. J. Heer, D. Moritz, Mosaic: An Architecture for Scalable & Interoperable Data Views, IEEE TVCG 30 (1) (2024).
  98. M. N. Hoque, S. Shin, N. Elmqvist, Visualization for Human-Centered AI Tools (2024). arXiv:2404.02147.
  99. C. D. Stolper, A. Perer, D. Gotz, Progressive Visual Analytics: User-Driven Visual Exploration of in-Progress Analytics, IEEE TVCG 20 (12) (2014).
  100. J.-D. Fekete, D. Fisher, M. Sedlmair, Progressive Data Analysis: Roadmap and Research Agenda, Eurographics, 2024.
  101. S. Castelo, R. Rampin, A. S. R. Santos, A. Bessa, F. Chirigati, J. Freire, Auctus: A Dataset Search Engine for Data Discovery and Augmentation, PVLDB 14 (12) (2021).
  102. N. Bikakis, S. Maroulis, G. Papastefanatos, P. Vassiliadis, In-situ Visual Exploration over Big Raw Data, Information Systems Journal 95 (2021).
  103. I. Alagiannis, R. Borovica, M. Branco, S. Idreos, A. Ailamaki, Nodb: Efficient Query Execution on Raw Data Files, in: ACM SIGMOD, 2012.
  104. H. Wickham, Bin-Summarise-Smooth: a Framework for Visualising Large Data, https://vita.had.co.nz/papers/bigvis.pdf (2013).
  105. L. Wilkinson, The Grammar of Graphics, Springer, 2005.
  106. A. Mayorga, M. Gleicher, Splatterplots: Overcoming Overdraw in Scatter Plots, IEEE TVCG 19 (9) (2013).
  107. M. Singh, A. Nandi, H. Jagadish, Skimmer: rapid scrolling of relational query results, in: ACM SIGMOD, 2012.
  108. A. Nandi, H. Jagadish, Guided interaction: Rethinking the query-result paradigm, PVLDB 4 (12) (2011).
  109. H. Yoo, A. Nandi, Emojis in autocompletion: Enhancing video search with visual cues, in: HILDA, 2025.
  110. H. Yoo, A. Nandi, Guided Querying over Videos Using Autocompletion Suggestions, in: HILDA, 2024.
  111. F. Romero, C. Winston, J. Hauswald, M. Zaharia, C. Kozyrakis, Zelda: Video analytics using vision-language models (2023). arXiv:2305.03785.
  112. R. Wu, P. Chunduri, D. J. Shah, A. J. Aravind, A. Payani, X. Chu, J. Arulraj, K. Rong, Sketchql demonstration: Zero-shot video moment querying with sketches (2024). arXiv:2405.18334.
  113. E. Zhang, M. Daum, D. He, B. Haynes, R. Krishna, M. Balazinska, Equi-vocal: Synthesizing queries for compositional video events from limited user interactions, PVLDB 16 (11) (2023).
  114. Y. Fu, R. Xie, X. Sun, Z. Kang, X. Li, Mitigating hallucination in multimodal large language model via hallucination-targeted direct preference optimization, in: Findings of the Association for Computational Linguistics: ACL 2025, 2025.
  115. D. Winecki, A. Nandi, V2V: Efficiently Synthesizing Video Results for Video Queries, in: IEEE ICDE, 2024.
  116. C. Burley, A. Nandi, Arquery: Hallucinating analytics over real-world data using augmented reality, in: CIDR, 2019.
  117. M. Khan, A. Nandi, Dreamstore: A Data Platform for Enabling Shared Augmented Reality, in: IEEE Virtual Reality and 3D User Interfaces (vr), 2021.
  118. K. Wolf, D. Winecki, A. Nandi, Camera-First Form Filling: Reducing the Friction in Climate Hazard Reporting, in: HILDA, 2023.
  119. A. Nandi, L. Jiang, M. Mandel, Gestural Query Specification, PVLDB 7 (4) (2013).
  120. S. Jo, I. Trummer, Thalamusdb: Approximate query processing on multi-modal data, ACM SIGMOD 2 (3) (2024).
  121. S. Basu Roy, H. Wang, G. Das, U. Nambiar, M. Mohania, Minimum-effort Driven Dynamic Faceted Search in Structured Databases, in: ACM CIKM, 2008.
  122. D. Mottin, A. Marascu, S. B. Roy, G. Das, T. Palpanas, Y. Velegrakis, A Probabilistic Optimization Framework for the Empty-Answer Problem, PVLDB 6 (14) (2013).
  123. M. M. Islam, M. Asadi, S. Amer-Yahia, S. B. Roy, A Generic Framework for Efficient Computation of top-k Diverse Results, VLDBJ 32 (4) (2023).
  124. S. Nikookar, M. Esfandiari, R. M. Borromeo, P. Sakharkar, S. Amer-Yahia, S. Basu Roy, Diversifying Recommendations on Sequences of Sets, VLDBJ 32 (2) (2023).
  125. M. R. Vieira, H. L. Razente, M. C. Barioni, M. Hadjieleftheriou, D. Srivastava, C. Traina, V. J. Tsotras, On Query Result Diversification, in: IEEE ICDE, 2011.
  126. V. Shah, S. Li, A. Kumar, L. Saul, Speakql: Towards Speech-Driven Multimodal Querying of Structured Data, in: ACM SIGMOD, 2020.
  127. D. Suciu, D. Olteanu, C. Ré, C. Koch, Probabilistic Databases, Springer, 2022.
  128. S. N. Nia, S. Ghosh, S. B. Roy, S. Amer-Yahia, Personalized Top-k Set Queries Over Predicted Scores (2025). arXiv:2502.12998.
  129. M. M. Islam, M. Asadi, S. Basu Roy, Equitable top-k Results for Long Tail Data, ACM SIGMOD 1 (4) (2023).
  130. P. Eades, A Heuristic for Graph Drawing, Congressus Numerantium 42 (1984).
  131. T. Kamada, S. Kawai, An Algorithm for Drawing General Undirected Graphs, Inf. Process. Lett. 31 (1) (1989).
  132. E. R. Gansner, Y. Koren, S. C. North, Graph Drawing by Stress Majorization, in: Graph Drawing, International Symposium (GD), Vol. 3383, 2004.
  133. Y. Wang, Z. Jin, Q. Wang, W. Cui, T. Ma, H. Qu, DeepDrawing: A Deep Learning Approach to Graph Drawing, IEEE TVCG 26 (1) (2020).
  134. O. Kwon, K. Ma, A Deep Generative Model for Graph Layout, IEEE TVCG 26 (1) (2020).
  135. X. Wang, K. Yen, Y. Hu, H. Shen, DeepGD: A Deep Learning Framework for Graph Drawing Using GNN, IEEE TVCG 41 (5) (2021).
  136. M. Tiezzi, G. Ciravegna, M. Gori, Graph Neural Networks for Graph Drawing, IEEE Trans. Neural Networks Learn. Syst. 35 (4) (2024).
  137. L. Giovannangeli, F. Lalanne, D. Auber, R. Giot, R. Bourqui, Toward Efficient Deep Learning for Graph Drawing (DL4GD), IEEE TVCG 30 (2) (2024).
  138. X. Wang, K. Yen, Y. Hu, H. Shen, SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals, IEEE TVCG 30 (8) (2024).
  139. K. Yan, T. Zhao, M. Yang, Graphuly: graph U-Nets-Based Multi-Level Graph LaYout, IEICE Trans. Inf. Syst. 105-D (12) (2022).
  140. F. Grötschla, J. Mathys, R. Veres, R. Wattenhofer, CoRe-gd: A Hierarchical Framework for Scalable Graph Visualization with gnns, in: Intl. Conf. on Learning Representations (ICLR), 2024.
  141. Z. Huang, S. Zhang, C. Xi, T. Liu, M. Zhou, Scaling Up Graph Neural Networks Via Graph Coarsening, in: ACM KDD, 2021.

BibTeX

@misc{fekete2026humanDataInteractionAI,
  title  = {Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities},
  author = {Fekete, Jean-Daniel and Hu, Yifan and Moritz, Dominik and Nandi, Arnab and Basu Roy, Senjuti and Wu, Eugene and Bikakis, Nikos and Papastefanatos, George and Chrysanthis, Panos K. and Li, Guoliang and Yu, Lingyun},
  year   = {2026},
  eprint = {2603.05542},
  archivePrefix = {arXiv}
}