Have a personal or library account? Click to login
Distance-based correlation analysis for graph databases Cover
By:
A. Dudáš and  J. Lauko  
Open Access
|Jun 2025

References

  1. Barrasa, J. and Webber, J. 2023. Building Knowledge Graphs, 1st ed. O’Reilly Media.
  2. 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.
  3. Cauterrucio, F. and Terracina, G. 2023. Extended high-utility pattern mining: An answer set programming-based framework and applications. Theory and Practice of Logic Programming.
  4. Cheng, J., Chen, M., and Liu, H. 2023. High dimensional binary classification under label shift: phase transition and regularization. Sampl. Theory Signal Process. Data Anal. 21, 32.
  5. Dudáš, A. and Kleinedler, A. 2024. Effective visualization of data structures in graph databases. Journal of Image and Graphics 12, 283–291.
  6. Ferilli, S., Bernasconi, E., D., D. P., and D., R. 2023. A graph db-based solution for semantic technologies in the future internet. Future Internet 15.
  7. Hribernik, M., Tomažič, S., and Umek, A. 2023. Unified platform for storing, retrieving, and analysing biomechanical applications data using graph database. Journal of Big Data 10.
  8. Ke, Y., Cheng, J., and Ng, W. 2008. Efficient correlation search from graph databases. IEEE Transactions on Knowledge and Data Engineering 20, 1601–1615.
  9. Latif, S., Z., M., G., R., F., R., N., A., and I., A. 2023. Pragmatic evidence of cross-language link detection: A systematic literature review. Journal of Systems and Software 206.
  10. Li, X., Fan, Y., and Lv, G. 2022. Area-based correlation and non-local attention network for stereo matching. Visual Computer.
  11. Liu, H., Chen, C., Li, Y., Duan, Z., and Li, Y. 2022. Characteristic and correlation analysis of metro loads. In Smart Metro Station Systems. Elsevier, 237–267.
  12. Monroy-Castillo, B., Jácome, M., and Cao, R. 2025. Improved distance correlation estimation. Applied Intelligence 55.
  13. Moskalenko, P. M. and Timchenko, V. A. 2023. Forming knowledge bases in accordance with ontological agreements on an intelligent systems development platform. Pattern Recognition and Image Analysis 33, 432–445.
  14. Samiullah, M., Ahmed, C., Nishi, M., Fariha, A., Abdullah, S., and Islam, M. 2013. Correlation mining in graph databases with a new measure. In Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol. 7808. Springer.
  15. Timón-Reina, S., Rincón, M., and Martínez-Tomás, R. 2021. An overview of graph databases and their applications in the biomedical domain. Database.
  16. Zhongtian, S., Harit, A., Cristea, A., Wang, J., and Lio, P. 2023. Money: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model. AI Open 4, 165–174.
  17. Zijie, Y., Haozhe, W., and Jia, J. 2022. Human motion modeling with deep learning: A survey. AI Open 3, 35–39.
DOI: https://doi.org/10.2478/jamsi-2025-0005 | Journal eISSN: 1339-0015 | Journal ISSN: 1336-9180
Language: English
Page range: 77 - 93
Published on: Jun 4, 2025
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2025 A. Dudáš, J. Lauko, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 License.