• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2016, Volume: 9, Issue: 35, Pages: 1-5

Original Article

Graph Mining Extensions in Postgresql


Objectives: To develop an extension for postgresql which will include the fundamental graph operations required for graph algorithms to allow moderately associated datasets to have both(graph and non-graph) concepts. Methods/ Statistical Analysis: The extension in postgresql uses the query optimizations of the SQL, although this can be done manually, so it was avoided. The algorithm used by the extension was a depth first search (DFS) algorithm. It is very simple and was written just to show the speed difference between using SQL and extensions. Findings: Relational databases are hard to work on graphs, as the tabular form cannot represent graphs well which Dedicated Graph databases like Neo4j, Titan and Allegrograph can and also provide performance when it comes to graph related queries which the relational databases deem complex and sometimes not possible.Common Table Expression (CTE) in postgresql perform graph related operations, but unable to perform more optimized and complex operations. To overcome this problem, we are going to develop extensions for postgresql. Application/Improvement: The time and cost i.e. the memory required is very less compared CTE’s.
Keywords: Allegro, Neo4j, Postgresql,Structured Databases,Titan 


Subscribe now for latest articles and news.