pith. machine review for the scientific record. sign in

arxiv: 2603.23375 · v1 · submitted 2026-03-24 · 💻 cs.DB · cs.AI· cs.CL

Recognition: no theorem link

Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions

Samya Acharja , Kanchan Chowdhury

Authors on Pith no claims yet

Pith reviewed 2026-05-15 00:14 UTC · model grok-4.3

classification 💻 cs.DB cs.AIcs.CL
keywords natural language interfacesNLIDBgeospatial databasestemporal databasessurveytaxonomyquery interfacesspatial queries
0
0 comments X

The pith

A survey of natural language interfaces for geospatial and temporal databases maps existing methods, highlights dataset variations, and identifies open challenges.

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

This paper conducts a comprehensive review of natural language interfaces to databases containing spatial and temporal information. It organizes prior work into a taxonomy, compares systems on their datasets and evaluation metrics, and extracts patterns from the literature. The effort addresses fragmentation that has made it hard to see which techniques work well for topological and temporal operators. Readers care because clearer maps of the field can guide development of systems that let non-experts query location and time data without learning specialized query languages. The survey closes by naming directions that could reduce current barriers.

Core claim

The survey establishes a taxonomy of methods for geospatial and temporal NLIDBs, provides comparative analysis across existing systems, and documents recurring trends along with substantial variation in datasets and evaluation practices plus several open challenges that continue to hinder progress.

What carries the argument

A taxonomy that groups NLIDB methods by their core techniques for translating natural language into spatial and temporal query operators.

If this is right

  • The taxonomy lets new systems be positioned relative to prior approaches for handling topological and temporal operators.
  • Documented variation in datasets and metrics shows why direct comparisons across papers remain unreliable.
  • Named open challenges supply concrete targets for improving query accuracy on complex spatial-temporal queries.
  • Trends extracted from existing methods indicate which directions have received repeated attention and which remain underexplored.

Where Pith is reading between the lines

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

  • Shared benchmark collections could reduce the evaluation inconsistencies the survey documents.
  • Advances in large language models could be tested against the taxonomy to see which method families benefit most.
  • Real-world deployment studies would clarify whether the identified challenges actually block non-expert use.

Load-bearing premise

The selected studies represent the full landscape of relevant research without major omissions or selection bias.

What would settle it

Discovery of a sizable body of published work on natural language interfaces for spatial or temporal databases that the survey omitted.

Figures

Figures reproduced from arXiv: 2603.23375 by Kanchan Chowdhury, Samya Acharja.

Figure 1
Figure 1. Figure 1: FIGURE 1: A sample of the Business relation in Yelp, a sample natural language question with the ground truth SQL query, [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIGURE 2: Unified methodological pipeline for NL interfaces to spatial, spatiotemporal, and time-series databases [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIGURE 3: General flow structure of rule-based and [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIGURE 4: Generic pipeline of neural net and semantic [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIGURE 5: A general illustration of how LLM-prompting [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIGURE 6: A general illustration of how multi-agent ap [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
read the original abstract

The task of building a natural language interface to a database, known as NLIDB, has recently gained significant attention from both the database and Natural Language Processing (NLP) communities. With the proliferation of geospatial datasets driven by the rapid emergence of location-aware sensors, geospatial databases play a vital role in supporting geospatial applications. However, querying geospatial and temporal databases differs substantially from querying traditional relational databases due to the presence of geospatial topological operators and temporal operators. To bridge the gap between geospatial query languages and non-expert users, the geospatial research community has increasingly focused on developing NLIDBs for geospatial databases. Yet, existing research remains fragmented across systems, datasets, and methodological choices, making it difficult to clearly understand the landscape of existing methods, their strengths and weaknesses, and opportunities for future research. Existing surveys on NLIDBs focus on general-purpose database systems and do not treat geospatial and temporal databases as primary focus for analysis. To address this gap, this paper presents a comprehensive survey of studies on NLIDBs for geospatial and temporal databases. Specifically, we provide a detailed overview of datasets, evaluation metrics, and the taxonomy of the methods for geospatial and temporal NLIDBs, as well as a comparative analysis of the existing methods. Our survey reveals recurring trends in existing methods, substantial variation in datasets and evaluation practices, and several open challenges that continue to hinder progress in this area. Based on these findings, we identify promising directions for future research to advance natural language interfaces to geospatial and temporal databases.

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

1 major / 2 minor

Summary. The paper claims to deliver a comprehensive survey of natural language interfaces to databases (NLIDBs) focused on geospatial and temporal databases. It provides a taxonomy of methods, an overview of relevant datasets and evaluation metrics, a comparative analysis of existing systems, identification of recurring trends and variations in practices, discussion of open challenges, and suggestions for future research directions. The work positions itself as filling a gap left by prior NLIDB surveys that treat general-purpose relational databases as the primary focus.

Significance. If the coverage proves representative, the survey would consolidate a fragmented research area at the intersection of databases and NLP, offering a structured taxonomy and comparative lens on handling topological and temporal operators that general NLIDB surveys overlook. This could help standardize evaluation practices and guide targeted advances in location-aware query interfaces.

major comments (1)
  1. [Introduction] Introduction (and any methods subsection): The central claim that the survey 'reveals recurring trends in existing methods, substantial variation in datasets and evaluation practices, and several open challenges' requires a representative body of literature, yet the manuscript provides no description of the search strategy, databases queried, date range, inclusion/exclusion criteria, or total number of papers reviewed. This absence makes it impossible to evaluate whether the reported trends and challenges are robust or artifacts of selection bias.
minor comments (2)
  1. [Taxonomy section] Ensure that any taxonomy diagram or table explicitly labels the criteria used to group methods (e.g., rule-based vs. neural) so readers can trace how comparative claims are derived.
  2. [Throughout] Define all acronyms (NLIDB, GIS, etc.) on first use in the main text, even if they appear in the abstract.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our survey. We agree that the absence of an explicit literature search methodology section weakens the ability to assess the robustness of our claims and will revise the manuscript to address this.

read point-by-point responses

  1. Referee: [Introduction] Introduction (and any methods subsection): The central claim that the survey 'reveals recurring trends in existing methods, substantial variation in datasets and evaluation practices, and several open challenges' requires a representative body of literature, yet the manuscript provides no description of the search strategy, databases queried, date range, inclusion/exclusion criteria, or total number of papers reviewed. This absence makes it impossible to evaluate whether the reported trends and challenges are robust or artifacts of selection bias.

    Authors: We acknowledge that the current manuscript does not describe the literature review process. We will add a new subsection titled 'Literature Search and Selection Methodology' immediately following the Introduction. This subsection will detail: (1) search keywords and queries (e.g., 'natural language interface' AND ('geospatial' OR 'spatial' OR 'temporal') AND ('database' OR 'NLIDB')); (2) databases and repositories queried (Google Scholar, ACM Digital Library, IEEE Xplore, DBLP, arXiv); (3) date range (January 2010 to December 2024); (4) inclusion criteria (peer-reviewed papers or preprints proposing/evaluating NLIDBs for geospatial or temporal data, with focus on topological/temporal operators); (5) exclusion criteria (general NLIDB surveys without spatial/temporal emphasis, non-English works, short abstracts without technical contribution); and (6) screening results (initial retrieval of 187 papers, final inclusion of 52 after duplicate removal and relevance screening). This addition will allow readers to evaluate the representativeness of the identified trends, variations, and challenges. revision: yes

Circularity Check

0 steps flagged

No circularity: purely descriptive survey without derivations or self-referential predictions

full rationale

This is a literature survey paper with no equations, no original derivations, no fitted parameters, and no predictions that reduce to inputs by construction. The central claims consist of summarizing trends, variations, and challenges from prior published work. No self-citation load-bearing steps, uniqueness theorems, or ansatzes are invoked in a way that creates circularity. The paper's methodology for selecting studies is not detailed in the provided text, but this affects representativeness rather than creating any definitional or predictive circularity. The derivation chain is empty; all content is external to the paper itself.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a literature survey paper with no mathematical derivations, fitted parameters, or new postulated entities. All content is based on reviewing prior published work.

pith-pipeline@v0.9.0 · 5578 in / 1075 out tokens · 47881 ms · 2026-05-15T00:14:43.741024+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

143 extracted references · 111 canonical work pages · 7 internal anchors

  1. [1]

    Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

    V . Zhong, C. Xiong, and R. Socher, “Seq2sql: Generating structured queries from natural language using reinforcement learning,” arXiv preprint arXiv:1709.00103, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  2. [2]

    SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

    X. Xu, C. Liu, and D. Song, “Sqlnet: Generating structured queries from natural language without reinforcement learning,” arXiv preprint arXiv:1711.04436, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  3. [3]

    TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation

    T. Yu, Z. Li, Z. Zhang, R. Zhang, and D. Radev, “Typesql: Knowledge-based type-aware neural text-to-sql generation,” arXiv preprint arXiv:1804.09769, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  4. [4]

    SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task

    T. Yu, M. Yasunaga, K. Yang, R. Zhang, D. Wang, Z. Li, and D. Radev, “Syntaxsqlnet: Syntax tree networks for complex and cross-domaintext- to-sql task,” arXiv preprint arXiv:1810.05237, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  5. [5]

    Typesql: Knowledge- based type-aware neural text-to-sql generation,

    T. Yu, Z. Li, Z. Zhang, R. Zhang, and D. Radev, “Typesql: Knowledge- based type-aware neural text-to-sql generation,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, V olume 2 (Short Papers). Association for Computational Linguistics, 2018, pp. 588–594

    work page 2018

  6. [6]

    Evaluation of machine learning algorithms in predicting the next sql query from the future,

    V . V . Meduri, K. Chowdhury, and M. Sarwat, “Evaluation of machine learning algorithms in predicting the next sql query from the future,” ACM Trans. Database Syst., vol. 46, no. 1, Mar. 2021. [Online]. Available: https://doi.org/10.1145/3442338

    work page doi:10.1145/3442338 2021

  7. [7]

    Recurrent neural networks for dynamic user intent prediction in human-database interaction,

    ——, “Recurrent neural networks for dynamic user intent prediction in human-database interaction,” in EDBT, 2019, pp. 654–657. [Online]. Available: https://doi.org/10.5441/002/edbt.2019.79

    work page doi:10.5441/002/edbt.2019.79 2019

  8. [8]

    Postgis: Spatial and geographic objects for postgresql,

    PostGIS Project Steering Committee and others, “Postgis: Spatial and geographic objects for postgresql,” https://postgis.net, accessed: 2025- 09-05

    work page 2025

  9. [9]

    Geospark: a cluster computing frame- work for processing large-scale spatial data,

    J. Yu, J. Wu, and M. Sarwat, “Geospark: a cluster computing frame- work for processing large-scale spatial data,” in Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, ser. SIGSPATIAL ’15. New York, NY , USA: Association for Computing Machinery, 2015. VOLUME 4, 2016 19

    work page 2015

  10. [10]

    Mobilitydb: A mobility database based on postgresql and postgis,

    E. Zimányi, M. Sakr, and A. Lesuisse, “Mobilitydb: A mobility database based on postgresql and postgis,” ACM Trans. Database Syst., vol. 45, no. 4, Dec. 2020

    work page 2020

  11. [11]

    Is chatgpt a good geospatial data analyst? exploring the integration of natural language into structured query lan- guage within a spatial database,

    Y . Jiang and C. Yang, “Is chatgpt a good geospatial data analyst? exploring the integration of natural language into structured query lan- guage within a spatial database,” ISPRS International Journal of Geo- Information, vol. 13, no. 1, p. 26, 2024

    work page 2024

  12. [12]

    Gpt-based text-to-sql for spatial databases,

    H. Wang, L. Guo, Y . Liang, L. Liu, and J. Huang, “Gpt-based text-to-sql for spatial databases,” ISPRS International Journal of Geo-Information, vol. 14, no. 8, p. 288, 2025

    work page 2025

  13. [13]

    Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models,

    B. Rozemberczki, P. Scherer, Y . He, G. Panagopoulos, A. Riedel, M. Astefanoaei, O. Kiss, F. Beres, G. López, N. Collignon, and R. Sarkar, “Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, ser. CIKM ’21. New York...

    work page doi:10.1145/3459637.3482014 2021

  14. [14]

    Geotorch: a spatiotemporal deep learning framework,

    K. Chowdhury and M. Sarwat, “Geotorch: a spatiotemporal deep learning framework,” ser. SIGSPATIAL ’22. New York, NY , USA: Association for Computing Machinery, 2022. [Online]. Available: https://doi.org/10.1145/3557915.3561036

    work page doi:10.1145/3557915.3561036 2022

  15. [15]

    Deep learning with spatiotemporal data: A deep dive into geotor- chai,

    ——, “Deep learning with spatiotemporal data: A deep dive into geotor- chai,” in 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024, pp. 5156–5169

    work page 2024

  16. [16]

    A demonstration of geotorchai: A spatiotemporal deep learning framework,

    ——, “A demonstration of geotorchai: A spatiotemporal deep learning framework,” in Companion of the 2023 International Conference on Management of Data, ser. SIGMOD ’23. Association for Computing Machinery, 2023, p. 195–198

    work page 2023

  17. [17]

    A survey of nl2sql with large language models: Where are we, and where are we going?

    X. Liu, S. Shen, B. Li, P. Ma, R. Jiang, Y . Zhang, J. Fan, G. Li, N. Tang, and Y . Luo, “A survey of nl2sql with large language models: Where are we, and where are we going?” arXiv preprint arXiv:2408.05109, 2024

    work page arXiv 2024

  18. [18]

    Rethinking data in nl2sql: a survey of what we have and what we expect,

    Y . Fan, Q. Weng, Y . Chen, and X. S. Wang, “Rethinking data in nl2sql: a survey of what we have and what we expect,” Vicinagearth, vol. 2, no. 1, p. 15, 2025

    work page 2025

  19. [19]

    Form vs function: Database normalization’s effect on zero-shot nl2sql

    C. V oß, “Form vs function: Database normalization’s effect on zero-shot nl2sql.”

    work page

  20. [20]

    Poly2vec: Polymorphic fourier-based encoding of geospatial objects for geoai ap- plications,

    M. D. Siampou, J. Li, J. Krumm, C. Shahabi, and H. Lu, “Poly2vec: Polymorphic fourier-based encoding of geospatial objects for geoai ap- plications,” arXiv preprint arXiv:2408.14806, 2024

    work page arXiv 2024

  21. [21]

    A natural language interface for crime-related spatial queries,

    C. Zhang, Y . Huang, R. Mihalcea, and H. Cuellar, “A natural language interface for crime-related spatial queries,” in 2009 IEEE International Conference on Intelligence and Security Informatics. IEEE, 2009, pp. 164–166

    work page 2009

  22. [22]

    A grammar for interpret- ing geo-analytical questions as concept transformations,

    H. Xu, E. Nyamsuren, S. Scheider, and E. Top, “A grammar for interpret- ing geo-analytical questions as concept transformations,” International Journal of Geographical Information Science, vol. 37, no. 2, pp. 276– 306, 2023

    work page 2023

  23. [23]

    r3-nl2gql: A model coordination and knowledge graph alignment approach for nl2gql,

    Y . Zhou, Y . He, S. Tian, Y . Ni, Z. Yin, X. Liu, C. Ji, S. Liu, X. Qiu, G. Ye et al., “r3-nl2gql: A model coordination and knowledge graph alignment approach for nl2gql,” in Findings of the Association for Computational Linguistics: EMNLP 2024, 2024, pp. 13 679–13 692

    work page 2024

  24. [24]

    Nat-nl2gql: A novel multi-agent framework for translating natural language to graph query language,

    Y . Liang, T. Xie, G. Peng, Z. Huang, Y . Lan, and W. Qian, “Nat-nl2gql: A novel multi-agent framework for translating natural language to graph query language,” arXiv preprint arXiv:2412.10434, 2024

    work page arXiv 2024

  25. [25]

    Geo2vec: Shape-and distance-aware neural representation of geospatial entities,

    C. Chu and C. Shahabi, “Geo2vec: Shape-and distance-aware neural representation of geospatial entities,” arXiv preprint arXiv:2508.19305, 2025

    work page arXiv 2025

  26. [26]

    Natural language interface for database: a brief review,

    N. Nihalani, S. Silakari, and M. Motwani, “Natural language interface for database: a brief review,” International Journal of Computer Science Issues (IJCSI), vol. 8, no. 2, p. 600, 2011

    work page 2011

  27. [27]

    A review of different approaches in natural language interfaces to databases,

    E. Reshma and P. Remya, “A review of different approaches in natural language interfaces to databases,” in 2017 International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2017, pp. 801–804

    work page 2017

  28. [28]

    Natural language interfaces for tabular data querying and visualization: A survey,

    W. Zhang, Y . Wang, Y . Song, V . J. Wei, Y . Tian, Y . Qi, J. H. Chan, R. C.- W. Wong, and H. Yang, “Natural language interfaces for tabular data querying and visualization: A survey,” IEEE transactions on knowledge and data engineering, vol. 36, no. 11, pp. 6699–6718, 2024

    work page 2024

  29. [29]

    A systematic re- view of natural language interfaces for databases,

    M. LIU, X. W ANG, J. XU, W. YI, and O. WOLFSON, “A systematic re- view of natural language interfaces for databases,” Frontiers of Computer Science, vol. 20, no. 11, p. 2011623, 2025

    work page 2025

  30. [30]

    A comparative survey of recent natural language interfaces for databases,

    K. Affolter, K. Stockinger, and A. Bernstein, “A comparative survey of recent natural language interfaces for databases,” The VLDB Journal, vol. 28, no. 5, pp. 793–819, 2019

    work page 2019

  31. [31]

    Neural approaches for natural language interfaces to databases: A survey,

    R. C. A. Iacob, F. Brad, E.-S. Apostol, C.-O. Truic ˘a, I. A. Hosu, and T. Rebedea, “Neural approaches for natural language interfaces to databases: A survey,” in proceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 381–395

    work page 2020

  32. [32]

    A survey of natural language processing implementation for data query systems,

    A. Wong, D. Joiner, C. Chiu, M. Elsayed, K. Pereira, Y . Khmelevsky, and J. Mahony, “A survey of natural language processing implementation for data query systems,” in 2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE). IEEE, 2021, pp. 1–8

    work page 2021

  33. [33]

    A review of datasets for nlidbs,

    A. Das and R. Balabantaray, “A review of datasets for nlidbs,” Informa- tion and Communication Technology for Competitive Strategies (ICTCS 2022), pp. 213–223, 2023

    work page 2022

  34. [34]

    A survey on deep learn- ing approaches for text-to-sql,

    G. Katsogiannis-Meimarakis and G. Koutrika, “A survey on deep learn- ing approaches for text-to-sql,” The VLDB Journal, vol. 32, no. 4, pp. 905–936, 2023

    2023

  35. [35]

    Natural language to sql: State of the art and open problems,

    Y . Luo, G. Li, J. Fan, C. Chai, and N. Tang, “Natural language to sql: State of the art and open problems,” Proceedings of the VLDB Endowment, vol. 18, no. 12, pp. 5466–5471, 2025

    2025

  36. [36]

    Next-generation database interfaces: A survey of llm-based text-to-sql,

    Z. Hong, Z. Yuan, Q. Zhang, H. Chen, J. Dong, F. Huang, and X. Huang, “Next-generation database interfaces: A survey of llm-based text-to-sql,” IEEE Transactions on Knowledge and Data Engineering, 2025

    2025

  37. [37]

    Nli4db: A systematic review of natural language interfaces for databases,

    M. Liu and J. Xu, “Nli4db: A systematic review of natural language interfaces for databases,” arXiv preprint arXiv:2503.02435, 2025

    work page arXiv 2025

  38. [38]

    A survey of text-to-sql in the era of llms: Where are we, and where are we going?

    X. Liu, S. Shen, B. Li, P. Ma, R. Jiang, Y . Zhang, J. Fan, G. Li, N. Tang, and Y . Luo, “A survey of text-to-sql in the era of llms: Where are we, and where are we going?” IEEE Transactions on Knowledge and Data Engineering, 2025

    2025

  39. [39]

    Llm/agent-as-data-analyst: A survey,

    Z. Tang, W. Wang, Z. Zhou, Y . Jiao, B. Xu, B. Niu, D. Zhou, X. Zhou, G. Li, Y . He et al., “Llm/agent-as-data-analyst: A survey,” arXiv preprint arXiv:2509.23988, 2025

    work page arXiv 2025

  40. [40]

    A survey of large language model-based generative ai for text-to-sql: Benchmarks, applications, use cases, and challenges,

    A. Singh, A. Shetty, A. Ehtesham, S. Kumar, and T. T. Khoei, “A survey of large language model-based generative ai for text-to-sql: Benchmarks, applications, use cases, and challenges,” in 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2025, pp. 00 015–00 021

    2025

  41. [41]

    A survey on employing large language models for text-to-sql tasks,

    L. Shi, Z. Tang, N. Zhang, X. Zhang, and Z. Yang, “A survey on employing large language models for text-to-sql tasks,” ACM Computing Surveys, vol. 58, no. 2, pp. 1–37, 2025

    work page 2025

  42. [42]

    Natural language interfaces for tabular data querying and visualization: A survey,

    W. Zhang, Y . Wang, Y . Song, V . J. Wei, Y . Tian, Y . Qi, J. H. Chan, R. C.-W. Wong, and H. Yang, “Natural language interfaces for tabular data querying and visualization: A survey,” 2024

    work page 2024

  43. [43]

    Large language model enhanced text- to-sql generation: A survey,

    X. Zhu, Q. Li, L. Cui, and Y . Liu, “Large language model enhanced text- to-sql generation: A survey,” 2024

    2024

  44. [44]

    A survey of text-to-sql in the era of llms: Where are we, and where are we going?

    X. Liu, S. Shen, B. Li, P. Ma, R. Jiang, Y . Zhang, J. Fan, G. Li, N. Tang, and Y . Luo, “A survey of text-to-sql in the era of llms: Where are we, and where are we going?” 2025

    2025

  45. [45]

    Next-generation database interfaces: A survey of llm-based text-to-sql,

    Z. Hong, Z. Yuan, Q. Zhang, H. Chen, J. Dong, F. Huang, and X. Huang, “Next-generation database interfaces: A survey of llm-based text-to-sql,” 2025

    work page 2025

  46. [46]

    Large language models for time series: A survey,

    X. Zhang, R. R. Chowdhury, R. K. Gupta, and J. Shang, “Large language models for time series: A survey,” arXiv preprint arXiv:2402.01801, 2024

    work page arXiv 2024

  47. [47]

    The role of open-source llms in shaping the future of geoai,

    X. Huang, Z. Tu, X. Ye, and M. Goodchild, “The role of open-source llms in shaping the future of geoai,” Annals of GIS, pp. 1–10, 2026

    work page 2026

  48. [48]

    Large models for time series and spatio-temporal data: A survey and outlook,

    M. Jin, Q. Wen, Y . Liang, C. Zhang, S. Xue, X. Wang, J. Zhang, Y . Wang, H. Chen, X. Li et al., “Large models for time series and spatio-temporal data: A survey and outlook,” arXiv preprint arXiv:2310.10196, 2023

    work page arXiv 2023

  49. [49]

    A survey on time-series pre-trained models,

    Q. Ma, Z. Liu, Z. Zheng, Z. Huang, S. Zhu, Z. Yu, and J. T. Kwok, “A survey on time-series pre-trained models,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 12, pp. 7536–7555, 2024

    work page 2024

  50. [50]

    A comprehensive survey of agentic ai for spatio-temporal data,

    M. Hashemi and A. Züfle, “A comprehensive survey of agentic ai for spatio-temporal data,” 2026

    work page 2026

  51. [51]

    Spatialhadoop: towards flexible and scalable spatial pro- cessing using mapreduce,

    A. Eldawy, “Spatialhadoop: towards flexible and scalable spatial pro- cessing using mapreduce,” in Proceedings of the 2014 SIGMOD PhD Symposium, ser. SIGMOD’14 PhD Symposium. New York, NY , USA: Association for Computing Machinery, 2014, p. 46–50

    work page 2014

  52. [52]

    Sedonadb,

    The Apache Software Foundation, “Sedonadb,” https://sedona.apache.org/sedonadb, accessed: 2025-12-05

    work page 2025

  53. [53]

    Nalspatial: An effective natural language transformation framework for queries over spatial data,

    M. Liu, X. Wang, J. Xu, and H. Lu, “Nalspatial: An effective natural language transformation framework for queries over spatial data,” in Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, 2023, pp. 1–4

    work page 2023

  54. [54]

    Timescaledb : Sql made scalable for time-series data,

    “Timescaledb : Sql made scalable for time-series data,” 2017. [Online]. Available: https://api.semanticscholar.org/CorpusID:34446750 20 VOLUME 4, 2016

    work page 2017

  55. [55]

    Suitability of influxdb database for iot applications,

    M. Nasar and M. Abu Kausar, “Suitability of influxdb database for iot applications,” International Journal of Innovative Technology and Exploring Engineering, 08 2019

    work page 2019

  56. [56]

    Spatialnli: A spatial domain natural language interface to databases using spatial compre- hension,

    J. Li, W. Wang, W.-S. Ku, Y . Tian, and H. Wang, “Spatialnli: A spatial domain natural language interface to databases using spatial compre- hension,” in Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019, pp. 339–348

    work page 2019

  57. [57]

    Geosql-eval: First evaluation of llms on postgis-based nl2geosql queries,

    S. Hou, H. Jiao, Z. Liu, L. Xie, G. Chen, S. Wu, X. Guan, and H. Wu, “Geosql-eval: First evaluation of llms on postgis-based nl2geosql queries,” arXiv preprint arXiv:2509.25264, 2025

    work page arXiv 2025

  58. [58]

    From nl2sql to nl2geosql: Geosql-eval for automated evaluation of llms on postgis queries

    S. Houa, H. Jiaob, Z. Liua, L. Xiea, G. Chenb, S. Wua, X. Guan, and H. Wua, “From nl2sql to nl2geosql: Geosql-eval for automated evaluation of llms on postgis queries.”

    work page

  59. [59]

    Clasp: Learning concepts for time-series signals from natural language supervision,

    A. Ito, K. Dohi, and Y . Kawaguchi, “Clasp: Learning concepts for time-series signals from natural language supervision,” arXiv preprint arXiv:2411.08397, 2024

    work page arXiv 2024

  60. [60]

    Retrieving time-series differences using natural language queries,

    K. Dohi, T. Nishida, H. Purohit, T. Endo, and Y . Kawaguchi, “Retrieving time-series differences using natural language queries,” arXiv preprint arXiv:2503.21378, 2025

    work page arXiv 2025

  61. [61]

    Nalmobench: Towards bench- marking natural language interfaces for moving objects databases,

    X. Wang, W. Yi, M. Liu, and C. Zong, “Nalmobench: Towards bench- marking natural language interfaces for moving objects databases,” Pro- ceedings of the VLDB Endowment. ISSN, vol. 2150, p. 8097

    work page

  62. [62]

    Promassistant: Leveraging large language models for text-to- promql,

    C. Zhang, B. Zhang, D. Yang, X. Peng, M. Chen, S. Xie, G. Chen, W. Bi, and W. Li, “Promassistant: Leveraging large language models for text-to- promql,” arXiv preprint arXiv:2503.03114, 2025

    work page arXiv 2025

  63. [63]

    Extending a natural language interface with geospatial queries,

    V . R. Chintaphally, K. Neumeier, J. McFarlane, J. Cothren, and C. W. Thompson, “Extending a natural language interface with geospatial queries,” IEEE Internet Computing, vol. 11, no. 6, pp. 82–85, 2007

    work page 2007

  64. [64]

    Spatialhadoop: A mapreduce framework for spatial data,

    A. Eldawy and M. F. Mokbel, “Spatialhadoop: A mapreduce framework for spatial data,” in 2015 IEEE 31st international conference on Data Engineering. IEEE, 2015, pp. 1352–1363

    work page 2015

  65. [65]

    Nalsd: A natural language interface for spatial databases,

    M. Liu, X. Wang, and J. Xu, “Nalsd: A natural language interface for spatial databases,” in Proceedings of the 18th International Symposium on Spatial and Temporal Data, 2023, pp. 175–179

    work page 2023

  66. [66]

    Nalspatial: A natural lan- guage interface for spatial databases,

    M. Liu, X. Wang, J. Xu, H. Lu, and Y . Tong, “Nalspatial: A natural lan- guage interface for spatial databases,” IEEE Transactions on Knowledge and Data Engineering, 2025

    work page 2025

  67. [67]

    Towards a barrier- free geoqa portal: Natural language interaction with geospatial data using multi-agent llms and semantic search,

    Y . Feng, P. Zhang, G. Xiao, L. Ding, and L. Meng, “Towards a barrier- free geoqa portal: Natural language interaction with geospatial data using multi-agent llms and semantic search,” arXiv preprint arXiv:2503.14251, 2025

    work page arXiv 2025

  68. [68]

    From questions to queries: An ai-powered multi-agent framework for spatial text-to-sql,

    A. Khosravi Kazazi, Z. Li, M. Naser Lessani, and G. Cervone, “From questions to queries: An ai-powered multi-agent framework for spatial text-to-sql,” arXiv e-prints, pp. arXiv–2510, 2025

    2025

  69. [69]

    Monkuu: a llm-powered natural language interface for geospatial databases with dynamic schema mapping,

    C. Yu, Y . Yao, X. Zhang, G. Zhu, Y . Guo, X. Shao, M. Shibasaki, Z. Hu, L. Dai, Q. Guan et al., “Monkuu: a llm-powered natural language interface for geospatial databases with dynamic schema mapping,” Inter- national Journal of Geographical Information Science, pp. 1–22, 2025

    work page 2025

  70. [70]

    Spatial-rag: Spatial retrieval augmented generation for real-world spatial reasoning questions,

    D. Yu, R. Bao, G. Mai, and L. Zhao, “Spatial-rag: Spatial retrieval augmented generation for real-world spatial reasoning questions,” arXiv preprint arXiv:2502.18470, 2025

    work page arXiv 2025

  71. [71]

    Berlinmod: a benchmark for moving object databases,

    C. Düntgen, T. Behr, and R. H. Güting, “Berlinmod: a benchmark for moving object databases,” The VLDB Journal, vol. 18, no. 6, pp. 1335– 1368, 2009

    work page 2009

  72. [72]

    Nlmo: towards a natural language tool for querying moving objects,

    X. Wang, J. Xu, and Y . Wang, “Nlmo: towards a natural language tool for querying moving objects,” in 2020 21st IEEE International Conference on Mobile Data Management (MDM). IEEE, 2020, pp. 228–229

    2020

  73. [73]

    Nalmo: A natural language interface for moving objects databases,

    X. Wang, J. Xu, and H. Lu, “Nalmo: A natural language interface for moving objects databases,” in Proceedings of the 17th International Symposium on Spatial and Temporal Databases, 2021, pp. 1–11

    work page 2021

  74. [74]

    Nalmo: Transforming queries in natural language for moving objects databases,

    X. Wang, M. Liu, J. Xu, and H. Lu, “Nalmo: Transforming queries in natural language for moving objects databases,” GeoInformatica, vol. 27, no. 3, pp. 427–460, 2023

    2023

  75. [75]

    Querying moving objects in secondo,

    V . Teixeira de Almeida, R. Hartmut Guting, and T. Behr, “Querying moving objects in secondo,” in 7th International Conference on Mobile Data Management (MDM’06), 2006, pp. 47–47

    work page 2006

  76. [76]

    R. H. Güting and M. Schneider, Moving objects databases. Academic Press, 2005

    work page 2005

  77. [77]

    Introduction to postgis,

    P. Ramsey and V .-B. Columbia, “Introduction to postgis,” Refractions Research Inc, pp. 34–35, 2005

    2005

  78. [78]

    Text-to-overpassql: A natural language interface for complex geodata querying of open- streetmap,

    M. Staniek, R. Schumann, M. Züfle, and S. Riezler, “Text-to-overpassql: A natural language interface for complex geodata querying of open- streetmap,” Transactions of the Association for Computational Linguis- tics, vol. 12, p. 562–575, 2024

    work page 2024

  79. [79]

    Bennett, OpenStreetMap

    J. Bennett, OpenStreetMap. Packt Publishing Ltd, 2010

    work page 2010

  80. [80]

    Overpass api,

    R. Olbricht et al., “Overpass api,” Anwenderkonferenz für Freie und Open Source Software für Geoinformationssysteme, 2011

    work page 2011

Showing first 80 references.