A systematic review of graph query language operators for graph analytics
Resumo
Graph analytics has gained significant relevance in recent years, enhancing the expressiveness of graph data systems by enabling advanced analytics within declarative query systems. In this context, graph query language operators have emerged to bridge the gap between graph processing and declarative querying. However, to the best of our knowledge, no prior work surveys advances in this area. We conduct a systematic review of graph query language operators for graph analytics, outlining their evolution, and classifying studies into five categories: domain-specific solutions, graph pattern matching and querying, graph mining, temporal graph analytics, and fuzzy and approximate querying. Finally, we discuss limitations, trends, and research opportunities.
Palavras-chave:
graph query languages, graph analytics, gql, cypher
Referências
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Aly, A., Pivert, O., and Thion, V. (2025). Fuzzy retrieval of musical scores based on melodic patterns. In 2025 IEEE FUZZ, pages 1–6. IEEE.
Anadiotis, A. C., Manolescu, I., and Mohanty, M. (2023a). Integrating connection search in graph queries. In 2023 IEEE 39th ICDE, pages 2607–2620. IEEE.
Anadiotis, A. C., Manolescu, I., and Mohanty, M. (2023b). More power to sparql: From paths to trees. In European Semantic Web Conference, pages 32–36. Springer.
Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J., and Vrgoč, D. (2017). Foundations of modern query languages for graph databases. ACM Computing Surveys (CSUR), 50(5):1–40.
Arenas, M., Bahamondes, P., Aghasadeghi, A., and Stoyanovich, J. (2022). Temporal regular path queries. In 2022 IEEE 38th ICDE, pages 2412–2425. IEEE.
Bamberg, B., Hirn, D., and Grust, T. (2025). How duckdb is using key to unlock recursive query performance. In Companion of the 2025 International Conference on Management of Data, pages 31–34.
Besta, M., Gerstenberger, R., Peter, E., Fischer, M., Podstawski, M., Barthels, C., Alonso, G., and Hoefler, T. (2023). Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries. ACM Computing Surveys, 56(2):1–40.
Bonifati, A. (2025). Versatile property graph transformations. In Proc.the VLDB Endowment, 18(12):5516–5526.
Bonifati, A., Iosup, A., Sakr, S., and Voigt, H. (2020). Big graph processing systems (dagstuhl seminar 19491). Dagstuhl Reports, 9(12):1–27.
Bonifati, A., Murlak, F., and Ramusat, Y. (2024a). Transforming property graphs. In Proc.the VLDB Endowment, 17(11):2906–2918.
Bonifati, A., Ozsu, M. T., Tian, Y., Voigt, H., Yu, W., and Zhang, e. (2025). A roadmap to graph analytics. ACM SIGMOD Record, 53(4):43–51.
Bonifati, A., Ramusat, Y., Murlak, F., Fejza, A., and Echahed, R. (2024b). Dt-graph: declarative transformations of property graphs. In Proc.the VLDB Endowment (PVLDB), 17(12):4265–4268.
Boukham, H., Younsi Dahbi, K., and Chiadmi, D. (2025). Domain-specific languages for algorithmic graph processing: A systematic literature review. Algorithms, 18(7):445.
Cambria, F., Invernici, F., Bernasconi, A., and Ceri, S. (2025). Mine graph rule: A new gql operator for mining association rules in property graph databases. The VLDB Journal, 34(4):54.
Debrouvier, A., Parodi, E., Perazzo, M., Soliani, V., and Vaisman, A. (2021). A model and query language for temporal graph databases. The VLDB Journal, 30(5):825–858.
Elshawi, R., Batarfi, O., Fayoumi, A., Barnawi, A., and Sakr, S. (2015). Big graph processing systems: State-of-the-art and open challenges. In 2015 IEEE First International Conference on Big Data Computing Service and Applications, pages 24–33. IEEE.
Ferrada, S., Bustos, B., and Hogan, A. (2024). Similarity joins and clustering for sparql. Semantic Web, 15(5):1701–1732.
Francis, N., Gheerbrant, A., Guagliardo, P., Libkin, L., Marsault, V., Martens, W., Murlak, F., Peterfreund, L., Rogova, A., and Vrgoc, D. (2023). A researcher’s digest of gql. In The 26th ICDT, 2023, pages 1–1. Schloss Dagstuhl-Leibniz-Zentrum für Informatik.
Gheerbrant, A., Libkin, L., Peterfreund, L., and Rogova, A. (2024). Gql and sql/pgq: Theoretical models and expressive power. arXiv preprint arXiv:2409.01102.
Ghrab, A., Romero, O., Jouili, S., and Skhiri, S. (2018). Graph bi & analytics: current state and future challenges. In In DAWAK 2018, pages 3–18. Springer.
Hogan, A., Reutter, J. L., and Soto, A. (2020). In-database graph analytics with recursive sparql. In International Semantic Web Conference, pages 511–528. Springer.
Hu, C., Zhao, Z., Mao, A., and Shen, Z. (2024). A model and query language for multi-modal hybrid query. In In Proc.the 36th International Conference on Scientific and Statistical Database Management, pages 1–4.
Jamshidi, K., Mariappan, M., and Vora, K. (2022). Anti-vertex for neighborhood constraints in subgraph queries. In In Proc.the 5th ACM SIGMOD GRADES-NDA Workshop, pages 1–9.
Jin, H., Qi, H., Zhao, J., Jiang, X., Huang, Y., Gui, C., Wang, Q., Shen, X., Zhang, Y., Hu, A., et al. (2022). Software systems implementation and domain-specific architectures towards graph analytics. Intelligent Computing.
Li, W., Peng, P., Qin, Z., and Zou, L. (2020a). Nrgqp: A graph-based query platform for network reachability. In International Conference on Web Information Systems Engineering, pages 55–63. Springer.
Li, X., Cheng, R., Najafi, M., Chang, K., Han, X., and Cao, H. (2020b). M-cypher: A gql framework supporting motifs. In In Proc.the 29th ACM CIKM, pages 3433–3436.
Lissandrini, M., Hose, K., and Pedersen, T. B. (2023). Example-driven exploratory analytics over knowledge graphs. In EDBT, pages 105–117.
Liu, S., Zeng, Z., Chen, L., Ainihaer, A., Ramasami, A., Chen, S., Xu, Y., Wu, M., and Wang, J. (2025). Tigervector: Supporting vector search in graph databases for advanced rags. In Companion of the 2025 International Conference on Management of Data, pages 553–565.
Liu, Y. (2022). A fuzzy query method oriented to knowledge graph. In Journal of Physics: Conference Series, volume 2221, page 012051. IOP Publishing.
Lu, R., Wang, C., Huang, X., and Zhang, S. (2020). B-sparql: A typed language for querying the big knowledge. In 2020 IEEE ICKG, pages 173–180. IEEE.
Nakagawa, E. Y., Scannavino, K. R. F., Fabbri, S. C. P. F., and Ferrari, F. C. (2017). Revisão sistemática da literatura em engenharia de software: teoria e prática. Elsevier Brasil.
Pacaci, A., Bonifati, A., and Özsu, M. T. (2022). Evaluating complex queries on streaming graphs. In 2022 IEEE 38th ICDE, pages 272–285. IEEE.
Pachera, A., Palmiotto, M., Bonifati, A., and Mauri, A. (2025). What if: Causal analysis with graph databases. In 51st VLDB, volume 18, pages 4009–4016. VLDB Endowment.
Pivert, O., Scholly, E., Smits, G., and Thion, V. (2020). Fuzzy quality-aware queries to graph databases. Information Sciences, 521:160–173.
Rost, C., Tommasini, R., Bonifati, A., Della Valle, E., Rahm, E., Hare, K. W., Plantikow, S., Voigt, H., and Selmer, P. (2024). Seraph: Continuous queries on property graph streams. In EDBT/ICDT 2025 Joint Conference.
Sahu, S., Mhedhbi, A., Salihoglu, S., Lin, J., and Özsu, M. T. (2020). The ubiquity of large graphs and surprising challenges of graph processing: extended survey: S. sahu et al. The VLDB journal, 29(2):595–618.
Singh, D. K. and Patgiri, R. (2016). Big graph: Tools, techniques, issues, challenges and future directions. In CS & IT Conference Proceedings, volume 6. CS & IT Conference Proceedings.
Terekhov, A., Pogozhelskaya, V., Abzalov, V., Zinnatulin, T., and Grigorev, S. V. (2021). Multiple-source context-free path querying in terms of linear algebra. In EDBT, pages 487–492.
Tommasini, R., Rost, C., Bonifati, A., Della Valle, E., Rahm, E., Hare, K. W., Plantikow, S., Selmer, P., and Voigt, H. (2024). Property graph stream processing in action with seraph. In Companion of the 2024 International Conference on Management of Data, pages 492–495.
Wang, R., Yang, Z., Zhang, W., and Lin, X. (2020). An empirical study on recent graph database systems. In International Conference on Knowledge Science, Engineering and Management, pages 328–340. Springer.
Yan, D., Bu, Y., Tian, Y., and Deshpande, A. (2017). Big graph analytics platforms. Foundations and Trends in Databases, 7(1-2):1–195.
Zhao, K., Su, J., Yu, J. X., and Zhang, H. (2021). Sql-g: Efficient graph analytics by sql. IEEE TKDE, 33(5):2237–2251.
Aly, A., Pivert, O., and Thion, V. (2025). Fuzzy retrieval of musical scores based on melodic patterns. In 2025 IEEE FUZZ, pages 1–6. IEEE.
Anadiotis, A. C., Manolescu, I., and Mohanty, M. (2023a). Integrating connection search in graph queries. In 2023 IEEE 39th ICDE, pages 2607–2620. IEEE.
Anadiotis, A. C., Manolescu, I., and Mohanty, M. (2023b). More power to sparql: From paths to trees. In European Semantic Web Conference, pages 32–36. Springer.
Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J., and Vrgoč, D. (2017). Foundations of modern query languages for graph databases. ACM Computing Surveys (CSUR), 50(5):1–40.
Arenas, M., Bahamondes, P., Aghasadeghi, A., and Stoyanovich, J. (2022). Temporal regular path queries. In 2022 IEEE 38th ICDE, pages 2412–2425. IEEE.
Bamberg, B., Hirn, D., and Grust, T. (2025). How duckdb is using key to unlock recursive query performance. In Companion of the 2025 International Conference on Management of Data, pages 31–34.
Besta, M., Gerstenberger, R., Peter, E., Fischer, M., Podstawski, M., Barthels, C., Alonso, G., and Hoefler, T. (2023). Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries. ACM Computing Surveys, 56(2):1–40.
Bonifati, A. (2025). Versatile property graph transformations. In Proc.the VLDB Endowment, 18(12):5516–5526.
Bonifati, A., Iosup, A., Sakr, S., and Voigt, H. (2020). Big graph processing systems (dagstuhl seminar 19491). Dagstuhl Reports, 9(12):1–27.
Bonifati, A., Murlak, F., and Ramusat, Y. (2024a). Transforming property graphs. In Proc.the VLDB Endowment, 17(11):2906–2918.
Bonifati, A., Ozsu, M. T., Tian, Y., Voigt, H., Yu, W., and Zhang, e. (2025). A roadmap to graph analytics. ACM SIGMOD Record, 53(4):43–51.
Bonifati, A., Ramusat, Y., Murlak, F., Fejza, A., and Echahed, R. (2024b). Dt-graph: declarative transformations of property graphs. In Proc.the VLDB Endowment (PVLDB), 17(12):4265–4268.
Boukham, H., Younsi Dahbi, K., and Chiadmi, D. (2025). Domain-specific languages for algorithmic graph processing: A systematic literature review. Algorithms, 18(7):445.
Cambria, F., Invernici, F., Bernasconi, A., and Ceri, S. (2025). Mine graph rule: A new gql operator for mining association rules in property graph databases. The VLDB Journal, 34(4):54.
Debrouvier, A., Parodi, E., Perazzo, M., Soliani, V., and Vaisman, A. (2021). A model and query language for temporal graph databases. The VLDB Journal, 30(5):825–858.
Elshawi, R., Batarfi, O., Fayoumi, A., Barnawi, A., and Sakr, S. (2015). Big graph processing systems: State-of-the-art and open challenges. In 2015 IEEE First International Conference on Big Data Computing Service and Applications, pages 24–33. IEEE.
Ferrada, S., Bustos, B., and Hogan, A. (2024). Similarity joins and clustering for sparql. Semantic Web, 15(5):1701–1732.
Francis, N., Gheerbrant, A., Guagliardo, P., Libkin, L., Marsault, V., Martens, W., Murlak, F., Peterfreund, L., Rogova, A., and Vrgoc, D. (2023). A researcher’s digest of gql. In The 26th ICDT, 2023, pages 1–1. Schloss Dagstuhl-Leibniz-Zentrum für Informatik.
Gheerbrant, A., Libkin, L., Peterfreund, L., and Rogova, A. (2024). Gql and sql/pgq: Theoretical models and expressive power. arXiv preprint arXiv:2409.01102.
Ghrab, A., Romero, O., Jouili, S., and Skhiri, S. (2018). Graph bi & analytics: current state and future challenges. In In DAWAK 2018, pages 3–18. Springer.
Hogan, A., Reutter, J. L., and Soto, A. (2020). In-database graph analytics with recursive sparql. In International Semantic Web Conference, pages 511–528. Springer.
Hu, C., Zhao, Z., Mao, A., and Shen, Z. (2024). A model and query language for multi-modal hybrid query. In In Proc.the 36th International Conference on Scientific and Statistical Database Management, pages 1–4.
Jamshidi, K., Mariappan, M., and Vora, K. (2022). Anti-vertex for neighborhood constraints in subgraph queries. In In Proc.the 5th ACM SIGMOD GRADES-NDA Workshop, pages 1–9.
Jin, H., Qi, H., Zhao, J., Jiang, X., Huang, Y., Gui, C., Wang, Q., Shen, X., Zhang, Y., Hu, A., et al. (2022). Software systems implementation and domain-specific architectures towards graph analytics. Intelligent Computing.
Li, W., Peng, P., Qin, Z., and Zou, L. (2020a). Nrgqp: A graph-based query platform for network reachability. In International Conference on Web Information Systems Engineering, pages 55–63. Springer.
Li, X., Cheng, R., Najafi, M., Chang, K., Han, X., and Cao, H. (2020b). M-cypher: A gql framework supporting motifs. In In Proc.the 29th ACM CIKM, pages 3433–3436.
Lissandrini, M., Hose, K., and Pedersen, T. B. (2023). Example-driven exploratory analytics over knowledge graphs. In EDBT, pages 105–117.
Liu, S., Zeng, Z., Chen, L., Ainihaer, A., Ramasami, A., Chen, S., Xu, Y., Wu, M., and Wang, J. (2025). Tigervector: Supporting vector search in graph databases for advanced rags. In Companion of the 2025 International Conference on Management of Data, pages 553–565.
Liu, Y. (2022). A fuzzy query method oriented to knowledge graph. In Journal of Physics: Conference Series, volume 2221, page 012051. IOP Publishing.
Lu, R., Wang, C., Huang, X., and Zhang, S. (2020). B-sparql: A typed language for querying the big knowledge. In 2020 IEEE ICKG, pages 173–180. IEEE.
Nakagawa, E. Y., Scannavino, K. R. F., Fabbri, S. C. P. F., and Ferrari, F. C. (2017). Revisão sistemática da literatura em engenharia de software: teoria e prática. Elsevier Brasil.
Pacaci, A., Bonifati, A., and Özsu, M. T. (2022). Evaluating complex queries on streaming graphs. In 2022 IEEE 38th ICDE, pages 272–285. IEEE.
Pachera, A., Palmiotto, M., Bonifati, A., and Mauri, A. (2025). What if: Causal analysis with graph databases. In 51st VLDB, volume 18, pages 4009–4016. VLDB Endowment.
Pivert, O., Scholly, E., Smits, G., and Thion, V. (2020). Fuzzy quality-aware queries to graph databases. Information Sciences, 521:160–173.
Rost, C., Tommasini, R., Bonifati, A., Della Valle, E., Rahm, E., Hare, K. W., Plantikow, S., Voigt, H., and Selmer, P. (2024). Seraph: Continuous queries on property graph streams. In EDBT/ICDT 2025 Joint Conference.
Sahu, S., Mhedhbi, A., Salihoglu, S., Lin, J., and Özsu, M. T. (2020). The ubiquity of large graphs and surprising challenges of graph processing: extended survey: S. sahu et al. The VLDB journal, 29(2):595–618.
Singh, D. K. and Patgiri, R. (2016). Big graph: Tools, techniques, issues, challenges and future directions. In CS & IT Conference Proceedings, volume 6. CS & IT Conference Proceedings.
Terekhov, A., Pogozhelskaya, V., Abzalov, V., Zinnatulin, T., and Grigorev, S. V. (2021). Multiple-source context-free path querying in terms of linear algebra. In EDBT, pages 487–492.
Tommasini, R., Rost, C., Bonifati, A., Della Valle, E., Rahm, E., Hare, K. W., Plantikow, S., Selmer, P., and Voigt, H. (2024). Property graph stream processing in action with seraph. In Companion of the 2024 International Conference on Management of Data, pages 492–495.
Wang, R., Yang, Z., Zhang, W., and Lin, X. (2020). An empirical study on recent graph database systems. In International Conference on Knowledge Science, Engineering and Management, pages 328–340. Springer.
Yan, D., Bu, Y., Tian, Y., and Deshpande, A. (2017). Big graph analytics platforms. Foundations and Trends in Databases, 7(1-2):1–195.
Zhao, K., Su, J., Yu, J. X., and Zhang, H. (2021). Sql-g: Efficient graph analytics by sql. IEEE TKDE, 33(5):2237–2251.
Publicado
08/09/2026
Como Citar
F. VASCONCELOS, Felipe; CATARIN, Renan B.; J. COUTINHO, Fábio; HALFELD-FERRARI, Mirian; AGUIAR, Cristina Dutra.
A systematic review of graph query language operators for graph analytics. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 275-288.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2026.249208.
