Design and Performance Evaluation of an Event-Driven Geolocation Data Enrichment Framework

11th International Scientific Conference Technics, Informatics and Education – TIE 2026, (pp. 162-169)

 

AUTOR(I) / AUTHOR(S): Veljko Lončarević and Željko Jovanović     

  

Download Full Pdf  

DOI: https://doi.org/10.46793/TIE26.162L

SAŽETAK /ABSTRACT:

Geolocation-based applications increasingly rely on multiple external services to enrich location information with administrative, environmental, and contextual data. As the volume of location requests grows, traditional synchronous service architectures face challenges related to scalability, latency, and database performance. This paper presents an event-driven geolocation data enrichment framework that combines multi-service API orchestration, Apache Kafka event streaming, hierarchical database partitioning, indexing strategies, and caching mechanisms. The proposed architecture processes incoming geolocation requests through a distributed enrichment pipeline, where independent microservices retrieve complementary location information from multiple free geolocation and open-data APIs. Enriched events are persisted within a hierarchically partitioned PostgreSQL database organized according to geographical structures (continent, region, country, and city). The study evaluates the impact of partitioning, indexing, and caching on query performance, response latency, and system throughput. Experimental results demonstrate that combining event-driven processing with database optimization techniques improves scalability and significantly reduces query execution times compared to a non-partitioned baseline architecture. The findings provide practical guidance for the design of efficient geolocation information systems and other event-driven spatial data processing platforms.

KLJUČNE REČI / KEYWORDS: 

Geolocation Services; Event-Driven Architecture; API Orchestration; Geospatial Data Enrichment; Database Partitioning

 PROJEKAT / ACKNOWLEDGEMENT:

LITERATURA / REFERENCES:

  • Haklay, M., & Weber, P. (2008). OpenStreetMap: User-generated street maps. IEEE Pervasive Computing, 7(4), 12–18.
    DOI: 10.1109/MPRV.2008.80.
  • Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69, 211–221.
    DOI: 10.1007/s10708-007-9111-y.
  • Rieke, M., Bigagli, L., Herle, S., Jirka, S., Kotsev, A., Liebig, T., Malewski, C., Paschke, T., & Stasch, C. (2018). Geospatial IoT—the need for event-driven architectures in contemporary spatial data infrastructures. ISPRS International Journal of Geo-Information, 7(10), 385.
    DOI: 10.3390/ijgi7100385.
  • Li, W., Liu, Y., & Wang, S. (2022). Real-time GIS programming and geocomputation. The Geographic Information Science & Technology Body of Knowledge, 2022(Q1).
    DOI: 10.22224/gistbok/2022.1.3.
  • Kreps, J., Narkhede, N., & Rao, J. (2011). Kafka: A distributed messaging system for log processing. Proceedings of the NetDB Workshop 2011.
  • Wang, G., Koshy, J., Subramanian, S., Paramasivam, K., Zadeh, M., Narkhede, N., Rao, J., Kreps, J., & Stein, J. (2015). Building a replicated logging system with Apache Kafka. Proceedings of the VLDB Endowment, 8(12).
  • Jafarpour, H., Desai, R., & Guy, D. (2019). KSQL: Streaming SQL engine for Apache Kafka. Proceedings of the 22nd International Conference on Extending Database Technology.
  • Akanbi, A. (2021). ESTemd: A distributed processing framework for environmental monitoring based on Apache Kafka streaming engine. arXiv preprint arXiv:2104.01082.
    DOI: 10.48550/arXiv.2104.01082.
  • Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, 47–57. DOI: 10.1145/602259.602266.
  • Hellerstein, J. M., Naughton, J. F., & Pfeffer, A. (1995). Generalized search trees for database systems. Proceedings of the 21st International Conference on Very Large Data Bases, 562–573.
  • Nguyen, T. T. (2009). Indexing PostGIS databases and spatial query performance evaluations. International Journal of Geoinformatics, 5(3), 1–9.
  • Rese, T. C., Kapp, A., & Bermbach, D. (2025). Evaluating the impact of spatial features of mobility data and index choice on database performance. Proceedings of the IEEE International Conference on Cloud Engineering. DOI: 10.1109/IC2E65552.2025.00007.
  • Privalov, M. V., & Stupina, M. V. (2024). Improving web-oriented information systems efficiency using Redis caching mechanisms. Indonesian Journal of Electrical Engineering and Computer Science, 33(3), 1667–1675. DOI: 10.11591/ijeecs.v33.i3.pp1667-1675.
  • Lončarević, V., & Jovanović, Ž. (2026). A service-oriented orchestration architecture for real-time maritime route monitoring. Proceedings of the 5th International Conference “Conference on Advances in Science and Technology” COAST 2026, Herceg Novi, Montenegro.
  • Lončarević, V., & Jovanović, Ž. (2025). Route-based geospatial clustering with unsupervised machine learning. 12th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN). DOI: 10.1109/IcETRAN66854.2025.11114188.
Post Views: 18