Relational graph data management on the edge: Grouping vertices’ neighborhood with Edge-k - Scabora, Lucas C.; Oliveira, Paulo H.; Kaster, Daniel S.; Traina, Agma J. M.; Traina-Jr, Caetano (Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2017)
As the amount of data represented as graph grows, several frameworks are employing relational databases to manage them. However, the existing solutions store graphs creating a row for each edge in an edge table. In this paper, we propose Edge-k, a novel storage approach that combines additional columns in the edges table, allowing to tune the number of edges stored in a single row by taking into account the overall neighborhood of the vertices, thus providing a better table organization. Compared to the existing approaches in the literature, experiments reveal that our proposal was able to reach a speedup of 66% over a representative real dataset and up to 57% in synthetic datasets when processing Single Source Shortest Path queries. Hence, our solution advances the state of the art in the context of graph data management within relational databases systems.