Learning to Speed Up Query Planning in Graph Databases

This paper is not directly related with our project, however it considers to improve query response times of the graph databases. Since we will use Neo4j graph database and have not direct access to its source code, we are dependent the way Neo4j plans computational steps of query processing. However, this paper’s focus is important to make the execution of queries more efficient especially in large graphs. Since querying in large graphs can be very slow in terms of response times, this paper presents a framework that is applicable to a large class of queries and claims a significant improvement in STAR graph query reasoner.