Similarity search over enriched geospatial data
Kostas Patroumpas, Dimitrios Skoutas · 2020
Enriched geospatial data refers to geospatial entities associated with additional information from various sources, such as textual, numerical or temporal. Exploring such data involves multi-criteria search and ranking across several heterogeneous attributes. In this paper, we model this task as a rank aggregation problem. Our method automatically scales similarity scores across diverse attributes without relying on user-specified parameters. It also allows to retrieve and combine information from multiple sources during query execution. We evaluate our approach using a large real-world dataset of enriched geospatial entities representing news articles.