Pith. sign in

REVIEW 4 major objections 6 minor 51 references

Text-to-TrajVis: Enabling Trajectory Data Visualizations from Natural Language Questions

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper introduces the Text-to-TrajVis task and TrajVL, the first large-scale benchmark pairing natural language questions with trajectory visualizations; it reports that current LLMs reach 74.61% accuracy on simple cases but 57.88% on…

desk verdict A genuinely new NL2VIS benchmark for trajectory data with real limitations around LLM-generated questions that need disclosure before the numbers can be trusted. read the letter →

arxiv 2504.16358 v1 pith:RHT6VZ5F submitted 2025-04-23 cs.CL

Tian Bai , Huiyan Ying , Kailong Suo , Junqiu Wei , Tao Fan , Yuanfeng Song This is my paper · ORCID
classification cs.CL
keywords Text-to-TrajVisTrajectoryVisualizationLanguageTrajVLnaturaltospatio-temporalreasoninglargemodelsdatabenchmarkdataset
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper establishes a new task, Text-to-TrajVis, in which a natural language question such as “show the trajectories in Beijing in March 2010” must be converted into a query that renders trajectory data as a map or chart. To make the task testable, the authors design a Trajectory Visualization Language (TVL) that packages visualization type, geographic area, time interval, and SQL query structure into one specification, and they build TrajVL, a benchmark of 18,140 (question, TVL) pairs generated from the GeoLife GPS dataset with LLM assistance and manual review. The evaluation of several large language models shows the task is feasible but not solved: the best model reaches 74.61% full-TVL accuracy on simple cases, while accuracy drops to about 57.88% when questions contain complex area descriptions. A sympathetic reader would care because this provides a concrete, reusable measure of how well LLMs can ground spatial and temporal language in executable data queries, a capability that matters for making trajectory data accessible to non-experts.

What carries the argument

The central object is the Trajectory Visualization Language (TVL), a compact formal language whose template combines a VISUALIZE type, an AREA parameter, a [ST, ET] time interval, and a SQL query skeleton with TRANSFORM and ORDER BY operators. The argument is carried by a construction pipeline: geographic areas and time ranges are harvested from GeoLife, seed TVLs are generated from a template, a constraint tree adds trajectory attributes such as travel mode and altitude, and LLMs write two or three paraphrased natural language questions per TVL, which are then manually checked and corrected. In evaluation, TVL accuracy—requiring visualization type, area, time, and SQL all to match—is the yardstick that makes the difficulty visible.

What would settle it

Ask independent human annotators to write free-form trajectory-visualization questions without seeing any TVL, translate those to TVL, and run the best few-shot model; if its accuracy is far below the 74.61% Normal-set score, the benchmark overstates real-world competence. A second check is to build the same benchmark from a different trajectory dataset with richer attributes; if model accuracy changes drastically, the constraint tree's attribute coverage, not LLM ability, is the binding factor.

Watch

Extended reading notes

Core claim

The central claim is that TrajVL is the first large-scale benchmark for Text-to-TrajVis and that it is hard enough to expose a clear gap in current LLMs. The discovery is that modern LLMs handle the surface syntax of the task—visualization-type detection is near 100%—but systematically confuse area names and complex time intervals when those must be carried into TVL. The best few-shot result, GPT-4o-mini at 74.61% TVL accuracy on the Normal test set, drops to 47.74% on the Area test set; the RAG-enhanced open-weight model reaches 57.88% on Area, still far below normal-case performance. The paper reads this as evidence that spatio-temporal reasoning, not code generation, is the bottleneck.

Load-bearing premise

The benchmark's results assume that the template-generated TVLs and the LLM-paraphrased questions represent how real users ask for trajectory visualizations; if real requests fall outside these templates, the reported accuracies measure self-consistency with the template distribution rather than true task competence.

Editorial extensions

If this is right

  • TrajVL gives the natural-language-to-visualization community a shared testbed for trajectory data, so future systems can be compared on the same 18,140 question–query pairs.
  • The near-perfect Vis.Acc across models means visualization-type detection is essentially solved, and research effort should shift to grounding area names and time intervals in the data.
  • RAG improves all models on every test set, but the Area test set remains the hardest, so retrieval of similar examples alone will not close the spatio-temporal reasoning gap.
  • Because TVL compiles to SQL with ST_Within and BETWEEN filters, correct TVL output is directly executable, which means the benchmark can support end-to-end evaluation from question to rendered map or chart.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the same construction pipeline were applied to richer trajectory sources with additional attributes, the constraint tree would grow and the benchmark would likely become harder; the paper's limitation section points in this direction.
  • The reported 74.61% ceiling suggests that training or fine-tuning on TrajVL, rather than prompting alone, is a natural next step, though the paper itself does not try this.
  • TVL could serve as a general intermediate representation for trajectory question answering beyond visualization, since it already encodes a spatio-temporal query; this is an extension the paper does not claim.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This paper defines a new task, Text-to-TrajVis, which maps natural-language questions about trajectory data to TVL, a compact declarative language for specifying map/bar/line/pie visualizations with area, time, SQL, and visualization-type components. The authors construct TrajVL, a dataset of 6,988 TVLs and 18,140 (question, TVL) pairs, by generating TVLs from a template/constraint tree over GeoLife data and using an unnamed LLM to paraphrase each TVL into 2–3 natural-language questions, followed by manual consistency checks. They evaluate four LLMs (GPT-4o-mini, Qwen2.5-7b, Llama3-8b, DeepSeek-llm-7b) under few-shot and RAG settings on Normal, Area, and Time test sets, using component-level and full TVL accuracy. The best reported full-TVL accuracy is 74.61% on Normal, 57.88% on Area, and 68.63% on Time, supporting the paper's conclusion that simple cases are tractable while complex spatio-temporal descriptions remain difficult.

Significance. Text-to-TrajVis is a plausible and potentially useful extension of NL2VIS to trajectory data, and the proposed TVL is a compact target representation that captures visualization type, area, time interval, and query structure. The paper's strengths are its clear pipeline description, honest limitation section (Section 7), multi-model evaluation, and decomposition of accuracy into Vis/Axis/Area/Time/SQL/TVL metrics. If the dataset is made available and its question distribution is shown to match real user requests, TrajVL would fill a genuine gap. However, the current evidence does not yet establish that the benchmark measures task competence beyond the template/paraphrase distribution: all questions were generated by an undisclosed LLM, test sets are drawn from the same template process as training, and no human-authored questions or out-of-template test data are provided. These issues bear directly on the central benchmark claim, so the contribution is promising but needs strengthening.

major comments (4)
  1. [Section 3.2 (Natural Language Questions Generation, Manual Check)] The manuscript never identifies the LLM used to generate the NLQs and gives no quantitative information about the manual validation (e.g., number of annotators, sampling procedure, or inter-annotator agreement). Because every NLQ is produced by prompting an LLM to paraphrase a gold TVL, and because GPT-4o-mini is among the evaluated models, the high accuracy on the Normal test set (Table 4, 74.61%) may reflect how well an evaluated model inverts the paraphrasing style of the generating model rather than how well it understands user requests. Please disclose the generator model, add a held-out set of human-written NLQs, and report a cross-model/cross-prompt agreement analysis to separate these effects.
  2. [Section 4 (Dataset Splitting) and Section 3.2 (Dataset Augmentation)] The Normal, Area, and Time test sets are constructed by the same template and constraint-tree process used to build the training set, so the test distribution is the training distribution (up to sampling). Without a structurally disjoint test set, the reported numbers are measures of self-consistency with the template distribution. The paper should add an out-of-template evaluation, e.g., questions collected from real users or generated by humans from trajectory-visualization scenarios, and report the performance drop relative to the template test sets.
  3. [Section 5 (Metrics) and Tables 3–4] The paper states that SQL accuracy is evaluated on the base SQL with area and time integrated afterward, so TVL accuracy is the only metric that requires complete spatio-temporal correctness. Given that Time.Acc and Area.Acc are often 60–99% while TVL accuracy is 20–30 points lower, an error analysis is needed to determine whether failures are caused by spatio-temporal semantics or by exact-match issues in the TVL format (e.g., ordering, quoting, or SQL variation). Without this decomposition, the claim that complex spatio-temporal descriptions specifically cause the failures is not fully supported. Please provide a breakdown of failure types and, if possible, a tolerance-based TVL matching metric.
  4. [General (dataset release)] The paper does not state where the TrajVL dataset, the generation prompts, or the evaluation code can be downloaded. For a benchmark paper whose central contribution is a dataset, a data-availability statement and a release link are necessary for the community to reproduce the results and use the benchmark. Please add them.
minor comments (6)
  1. [Section 2.3] In the last paragraph, 'TrajVis' appears where the dataset name 'TrajVL' is intended; please correct this.
  2. [Section 4] Please clarify whether the 5,012 training and 1,973 test 'instances' are TVLs or (question, TVL) pairs; the numbers do not sum to the 18,140 pairs stated in Table 1.
  3. [Appendix B.2] In the metric definitions, N is described both as 'the total number of visualization types' and 'the total number of queries'; please use consistent terminology.
  4. [Tables 3–6] No confidence intervals or multiple-run variability are reported; with temperature 0.1 this may be modest, but a note on variance would help interpret small differences between models.
  5. [Section 2.3] The sentence 'Experimental and human evaluations validate the utility of this dataset' is not supported by any reported human evaluation in the experimental section; remove it or add the corresponding study.
  6. [Appendix A.4 and A.5] The NLQ generation and correction prompts are only mentioned as figures; include the actual prompt text in the supplemental material so the data-construction process is reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the TrajVL benchmark construction and evaluation are self-contained, and the acknowledged limitations concern external validity rather than logical circularity.

full rationale

The paper's chain is: define the TVL template from GeoLife trajectory attributes (Section 3.1); instantiate TVLs from collected areas and time ranges; augment them via a constraint tree (Section 3.2); use an LLM to paraphrase each gold TVL into 2-3 natural language questions; manually check fidelity; then hold out a stratified test set and evaluate four LLMs. No step defines the measured quantity in terms of the models being evaluated. Gold TVLs are produced by the template/tree construction, not by the tested models, and the test split is held out, so the accuracies in Tables 3-4 are not fitted values renamed as predictions. The closest validity concern is that LLM-generated NLQs may not match real user requests; the paper explicitly acknowledges this in Section 7 ('the Area Test and Time Test cannot comprehensively cover all real-world situations with complex descriptions of spatio-temporal information'), but that is a construct/external-validity limitation, not a circular reduction. The manual check verifies NLQ-to-TVL fidelity but does not establish the user-query distribution; again, this weakens generalization claims without making the reported numbers logically entailed by the construction. Self-citations to Song et al. (2022, 2024) appear only as related work and are not load-bearing for the central benchmark claim. No self-definitional step, fitted-input-called-prediction, uniqueness theorem, or ansatz-smuggled-via-citation was found.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The central benchmark claim relies on the quality of the GeoLife-derived trajectory data, the validity of the template-expansion approach, and the reliability of LLM-generated questions after human review. The evaluation itself depends on chosen few-shot counts and retrieval settings. These are design choices rather than physical constants, and none are derived from first principles.

free parameters (2)
  • Per-model optimal few-shot count = DeepSeek-llm-7b: 4; Llama3-8b: 5; Qwen2.5-7b: 6; GPT-4o-mini: 6
    Chosen from performance curves in Appendix C.1; using each model's best count gives favorable headline accuracy and prevents controlled comparison at a fixed shot count.
  • RAG top-k retrieval depth = 1 to 3
    The retrieval depth is varied over 1 to 3 (Appendix B.3) and the reported RAG results use the best-performing configuration, which can inflate the apparent benefit of RAG.
assumptions (4)
  • domain assumption GeoLife GPS trajectories and OpenStreetMap boundaries are accurate enough to serve as ground-truth spatial-temporal data.
    Section 3.1 builds all TVLs and NLQs from these sources without independent verification of boundary or trajectory correctness.
  • domain assumption LLM-generated natural language questions, after prompting-based correction and manual review, are semantically consistent with their TVLs.
    Section 3.2 reports issue rates (11.96%, 13.1%, 8.4%) but no inter-annotator agreement or independent gold labels, so the final quality rests on the validation procedure described.
  • ad hoc to paper The hand-designed constraint tree enumerates a representative space of trajectory queries.
    Section 3.2 builds leaf-node constraints from user ID, travel mode, and altitude only; no evidence this covers real query diversity.
  • domain assumption Exact-match evaluation of Area, Time, and SQL components is a valid measure of visualization correctness.
    Appendix B.2 defines accuracy as exact component matching, which penalizes equally correct paraphrases and may not reflect visualization quality.
invented entities (1)
  • Trajectory Visualization Language (TVL)
    purpose: A structured intermediate representation that converts natural-language trajectory queries into visualization code and SQL.
    TVL is precisely specified with a grammar and templates (Figure 2, Appendix A) and can be compiled to SQL/Vega-Lite, giving an executable handle; however, it is only defined within this paper and has no external adoption or independent validation yet.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Text-to-TrajVis: Enabling Trajectory Data Visualizations from Natural Language Questions." pith.science (2026). https://pith.science/paper/RHT6VZ5F

@misc{pith2026250416358,
  author       = {Pith},
  title        = {Pith review of: Text-to-TrajVis: Enabling Trajectory Data Visualizations from Natural Language Questions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RHT6VZ5F}},
  note         = {Machine review of arXiv:2504.16358}
}
read the original abstract

This paper introduces the Text-to-TrajVis task, which aims to transform natural language questions into trajectory data visualizations, facilitating the development of natural language interfaces for trajectory visualization systems. As this is a novel task, there is currently no relevant dataset available in the community. To address this gap, we first devised a new visualization language called Trajectory Visualization Language (TVL) to facilitate querying trajectory data and generating visualizations. Building on this foundation, we further proposed a dataset construction method that integrates Large Language Models (LLMs) with human efforts to create high-quality data. Specifically, we first generate TVLs using a comprehensive and systematic process, and then label each TVL with corresponding natural language questions using LLMs. This process results in the creation of the first large-scale Text-to-TrajVis dataset, named TrajVL, which contains 18,140 (question, TVL) pairs. Based on this dataset, we systematically evaluated the performance of multiple LLMs (GPT, Qwen, Llama, etc.) on this task. The experimental results demonstrate that this task is both feasible and highly challenging and merits further exploration within the research community.

Figures

Figures reproduced from arXiv: 2504.16358 by the authors.

Figure 1
Figure 1. The process of transforming natural language [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The structure of Trajectory Visualization Lan [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The visualization workflow for Trajectory [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: The pipeline of constructing the TrajVL dataset. The construction of TrajVL involves four main steps: [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Examples of the visualization specification in Vega-Lite, and its corresponding visualization. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Templates for extended visualization types built from additional trajectory data. [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: An example of Synthesizing SQL based on TVL. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: The prompts for generating NLQs. We designed prompt templates for LLM. These templates were categorized into three different types: (i) basic data generation: requiring that NLQs maintain semantic accuracy; (ii) diversification of area descriptions: including synonymou…
Figure 9
Figure 9. Figure 9: The prompts for correcting NLQs [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: The prompt for LLMs to generate TVLs in experiment. [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Average performance of LLMs on three test sets. [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

51 extracted references · 38 canonical work pages

  1. [1]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774

    arXiv 2023

  2. [2]

    Hossein Amiri, Ruochen Kong, and Andreas Z \"u fle. 2024 a . Urban anomalies: A simulated human mobility dataset with injected anomalies. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection, pages 1--11

    work page 2024

  3. [3]

    Hossein Amiri, Richard Yang, and Andreas Z \"u fle. 2024 b . Geolife+: Large-scale simulated trajectory datasets calibrated to the geolife dataset. In Proceedings of the 7th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, pages 25--28

    work page 2024

  4. [4]

    Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng, Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, et al. 2024. Deepseek llm: Scaling open-source language models with longtermism. arXiv preprint arXiv:2401.02954

    arXiv 2024

  5. [5]

    Howard Butler, Martin Daly, Allan Doyle, Sean Gillies, Stefan Hagen, and Tim Schaub. 2016. The geojson format. Technical report

    work page 2016

  6. [6]

    Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2024. Scaling instruction-finetuned language models. Journal of Machine Learning Research, 25(70):1--53

    2024

  7. [7]

    Kenneth Cox, Rebecca E Grinter, Stacie L Hibino, Lalita Jategaonkar Jagadeesan, and David Mantilla. 2001. A multi-modal natural language interface to an information visualization environment. International Journal of Speech Technology, 4:297--314

    work page 2001

  8. [8]

    Paul Crickard III. 2014. Leaflet. js essentials. Packt Publishing Ltd

    work page 2014

Show all 51 references
  1. [9]

    Victor Dibia and C a g atay Demiralp. 2019. Data2vis: Automatic generation of data visualizations using sequence-to-sequence recurrent neural networks. IEEE computer graphics and applications, 39(5):33--46

    2019

  2. [10]

    Xuemei Dong, Chao Zhang, Yuhang Ge, Yuren Mao, Yunjun Gao, Jinshu Lin, Dongfang Lou, et al. 2023. C3: Zero-shot text-to-sql with chatgpt. arXiv preprint arXiv:2307.07306

    2023 arXiv

  3. [11]

    Phil Elson, Elliott Sales De Andrade, Greg Lucas, Ryan May, Richard Hattersley, Ed Campbell, Andrew Dawson, Stephane Raynaud, Bill Little, Alan D Snow, et al. 2022. Scitools/cartopy: v0. 21.0. Zenodo

    2022

  4. [12]

    Yujian Gan, Xinyun Chen, Jinxia Xie, Matthew Purver, John R Woodward, John Drake, and Qiaofu Zhang. 2021. Natural sql: Making sql easier to infer from natural language specifications. arXiv preprint arXiv:2109.05153

    2021 arXiv

  5. [13]

    Dawei Gao, Haibin Wang, Yaliang Li, Xiuyu Sun, Yichen Qian, Bolin Ding, and Jingren Zhou. 2023. Text-to-sql empowered by large language models: A benchmark evaluation. arXiv preprint arXiv:2308.15363

    2023 arXiv

  6. [14]

    Tong Gao, Mira Dontcheva, Eytan Adar, Zhicheng Liu, and Karrie G Karahalios. 2015. Datatone: Managing ambiguity in natural language interfaces for data visualization. In Proceedings of the 28th annual acm symposium on user interface software & technology, pages 489--500

    2015

  7. [15]

    Sean Gillies, Casper van der Wel, Joris Van den Bossche, Mike W Taves, Joshua Arnott, Brendan C Ward, et al. 2023. Shapely. Zenodo

    2023

  8. [16]

    Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. 2024. The llama 3 herd of models. arXiv e-prints, pages arXiv--2407

    2024

  9. [17]

    Mordechai Haklay and Patrick Weber. 2008. Openstreetmap: User-generated street maps. IEEE Pervasive computing, 7(4):12--18

    2008

  10. [18]

    Jing He, Haonan Chen, Yijin Chen, Xinming Tang, and Yebin Zou. 2019. Diverse visualization techniques and methods of moving-object-trajectory data: A review. ISPRS International Journal of Geo-Information, 8(2):63

    2019

  11. [19]

    James N Hughes, Andrew Annex, Christopher N Eichelberger, Anthony Fox, Andrew Hulbert, and Michael Ronquest. 2015. Geomesa: a distributed architecture for spatio-temporal fusion. In Geospatial informatics, fusion, and motion video analytics V, volume 9473, pages 128--140. SPIE

    2015

  12. [20]

    Kelsey Jordahl, Joris Van den Bossche, Jacob Wasserman, James McBride, Jeffrey Gerard, Jeff Tratner, Matthew Perry, and Carson Farmer. 2021. geopandas/geopandas: v0. 5.0. Zenodo

    2021

  13. [21]

    Will Kohn, Hossein Amiri, and Andreas Z \"u fle. 2023. Epipol: An epidemiological patterns of life simulation (demonstration paper). In Proceedings of the 4th ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology, pages 13--16

    2023

  14. [22]

    Maximilian Konzack, Pieter Gijsbers, Ferry Timmers, Emiel van Loon, Michel A Westenberg, and Kevin Buchin. 2019. Visual exploration of migration patterns in gull data. Information Visualization, 18(1):138--152

    2019

  15. [23]

    Michael Law and Amy Collins. 2019. Getting to know arcgis pro. (No Title)

    2019

  16. [24]

    Carolin Lawrence and Stefan Riezler. 2018. Improving a neural semantic parser by counterfactual learning from human bandit feedback. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1820--1830

    2018

  17. [25]

    Haoyang Li, Jing Zhang, Cuiping Li, and Hong Chen. 2023 a . Resdsql: Decoupling schema linking and skeleton parsing for text-to-sql. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 13067--13075

    2023

  18. [26]

    Jinyang Li, Binyuan Hui, Reynold Cheng, Bowen Qin, Chenhao Ma, Nan Huo, Fei Huang, Wenyu Du, Luo Si, and Yongbin Li. 2023 b . Graphix-t5: Mixing pre-trained transformers with graph-aware layers for text-to-sql parsing. In Proceedings of the AAAI conference on artificial intell...

    2023

  19. [27]

    Can Liu, Yun Han, Ruike Jiang, and Xiaoru Yuan. 2021. Advisor: Automatic visualization answer for natural-language question on tabular data. In 2021 IEEE 14th Pacific Visualization Symposium (PacificVis), pages 11--20. IEEE

    2021

  20. [28]

    Hu Liu, Yuliang Shi, Jianlin Zhang, Xinjun Wang, Hui Li, and Fanyu Kong. 2023. Multi-hop relational graph attention network for text-to-sql parsing. In 2023 International Joint Conference on Neural Networks (IJCNN), pages 1--8. IEEE

    2023

  21. [29]

    Yuyu Luo, Nan Tang, Guoliang Li, Chengliang Chai, Wenbo Li, and Xuedi Qin. 2021 a . Synthesizing natural language to visualization (nl2vis) benchmarks from nl2sql benchmarks. In Proceedings of the 2021 International Conference on Management of Data, pages 1235--1247

    2021

  22. [30]

    Yuyu Luo, Nan Tang, Guoliang Li, Jiawei Tang, Chengliang Chai, and Xuedi Qin. 2021 b . Natural language to visualization by neural machine translation. IEEE Transactions on Visualization and Computer Graphics, 28(1):217--226

    2021

  23. [31]

    Paula Maddigan and Teo Susnjak. 2023. Chat2vis: Generating data visualizations via natural language using chatgpt, codex and gpt-3 large language models. Ieee Access, 11:45181--45193

    2023

  24. [32]

    Mark Mucchetti. 2020. BigQuery for Data Warehousing. Springer

    2020

  25. [33]

    Arpit Narechania, Arjun Srinivasan, and John Stasko. 2020. Nl4dv: A toolkit for generating analytic specifications for data visualization from natural language queries. IEEE Transactions on Visualization and Computer Graphics, 27(2):369--379

    2020

  26. [34]

    Regina Obe and Leo Hsu. 2021. PostGIS in action. Simon and Schuster

    2021

  27. [35]

    Mohammadreza Pourreza and Davood Rafiei. 2023. Din-sql: Decomposed in-context learning of text-to-sql with self-correction. Advances in Neural Information Processing Systems, 36:36339--36348

    2023

  28. [36]

    Yuanfeng Song, Xuefang Zhao, and Raymond Chi-Wing Wong. 2024. Marrying dialogue systems with data visualization: Interactive data visualization generation from natural language conversations. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mini...

    2024

  29. [37]

    Yuanfeng Song, Xuefang Zhao, Raymond Chi-Wing Wong, and Di Jiang. 2022. Rgvisnet: A hybrid retrieval-generation neural framework towards automatic data visualization generation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1646--1655

    2022

  30. [38]

    Michael Staniek, Raphael Schumann, Maike Z \"u fle, and Stefan Riezler. 2024. Text-to-overpassql: A natural language interface for complex geodata querying of openstreetmap. Transactions of the Association for Computational Linguistics, 12:562--575

    2024

  31. [39]

    KG Suma, Gurram Sunitha, J Avanija, Mohammad Gouse Galety, and Chinthapatla Pranay Varna. 2024. Geospatial data visualization with folium. In Geospatial Application Development Using Python Programming, pages 187--208. IGI global

    2024

  32. [40]

    Yuan Tian, Weiwei Cui, Dazhen Deng, Xinjing Yi, Yurun Yang, Haidong Zhang, and Yingcai Wu. 2024. Chartgpt: Leveraging llms to generate charts from abstract natural language. IEEE Transactions on Visualization and Computer Graphics

    2024

  33. [41]

    Eran Toch, Boaz Lerner, Eyal Ben-Zion, and Irad Ben-Gal. 2019. Analyzing large-scale human mobility data: a survey of machine learning methods and applications. Knowledge and Information Systems, 58:501--523

    2019

  34. [42]

    Yanzheng Xiang, Qian-Wen Zhang, Xu Zhang, Zejie Liu, Yunbo Cao, and Deyu Zhou. 2023. G3r: A graph-guided generate-and-rerank framework for complex and cross-domain text-to-sql generation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 338--352

    2023

  35. [43]

    An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. 2024. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115

    2024 arXiv

  36. [44]

    Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 2018. Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task. In Proceedings of the 2018 Con...

    2018

  37. [45]

    Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, and Yan Huang. 2010. https://api.semanticscholar.org/CorpusID:7077350 T-drive: driving directions based on taxi trajectories . In ACM SIGSPATIAL International Workshop on Advances in Geographic Informa...

    2010

  38. [46]

    John M Zelle and Raymond J Mooney. 1996. Learning to parse database queries using inductive logic programming. In Proceedings of the national conference on artificial intelligence, pages 1050--1055

    1996

  39. [47]

    Lijing Zhang and Jing Yi. 2010. Management methods of spatial data based on postgis. In 2010 Second Pacific-Asia Conference on Circuits, Communications and System, volume 1, pages 410--413. IEEE

    2010

  40. [48]

    Yu Zheng, Hao Fu, Xing Xie, Wei Ying Ma, and Quannan Li. 2011. Geolife gps trajectory dataset - user guide

    2011

  41. [49]

    Feng Zhu, Chen Chang, Zhiheng Li, Boqi Li, and Li Li. 2024 a . https://api.semanticscholar.org/CorpusID:268438851 A generic optimization-based enhancement method for trajectory data: Two plus one. Accident; analysis and prevention, 200:107532

    2024

  42. [50]

    Yuanshao Zhu, James Jian Qiao Yu, Xiangyu Zhao, Xuetao Wei, and Yuxuan Liang. 2024 b . https://api.semanticscholar.org/CorpusID:273850242 Unitraj: Learning a universal trajectory foundation model from billion-scale worldwide traces . ArXiv, abs/2411.03859

    2024

  43. [51]

    Esteban Zim \'a nyi, Mahmoud Sakr, and Arthur Lesuisse. 2020. Mobilitydb: A mobility database based on postgresql and postgis. ACM Transactions on Database Systems (TODS), 45(4):1--42

    2020

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.