Approximate Querying for the Property Graph Language Cypher

Graph databases are well-suited to managing large, complex, dynamically evolving datasets. However, for data that is irregular and heterogeneous, it may be difficult to formulate queries that precisely capture a user's information seeking requirements. This points to the need for approximate query processing capabilities that can automatically make changes to a query so as to aid in the incremental discovery of relevant information. In this paper we motivate and explore techniques for providing such capabilities for the Cypher query language. This is the first time that query approximation has been investigated in the context of the property graph data model, which is becoming increasingly prevalent in research and industry.