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Indexing Patterns in Graph Databases

Nowadays graphs have become very popular in domains like social media analytics, healthcare, natural sciences, BI, networking, graph-based bibliographic IR, etc. Graph databases (GDB) allow simple and rapid retrieval of complex graph structures that are difficult to model in traditional IS based on a relational DBMS. GDB are designed to exploit relationships in data, which means they can uncover patterns difficult to detect using traditional methods. We introduce a new method for indexing graph patterns within a GDB modelled as a labelled property graph. The index is organized in a tree structure and stored in the same database where the database graph. The method is analysed and implemented for Neo4j GDB engine. It enables to create, use and update indexes that are used to speed-up the process of matching graph patterns. The paper provides a comparison between queries with and without using indexes.