Graph Pattern Matching: A Brief Survey of Challenges and Research Directions
Komal Singh, Vikram Singh · 2016
Due to tremendous intensification of Internet, most of the data is archived and analyzed in graph-structured database. Graph Database is a collection of operational data objects, mapped into huge labeled graph or a set of labeled directed graphs. In recent years, modeling data in graph structure becomes evident and effective for processing in some of the prominent application area like social analytics etc. Graph databases are mapped into data graphs for further computing or processing. Graphs are one of the dominant data modeling tool used in many application areas. Hence, there is a demand for proficient querying techniques on such large data graphs. The user query is mapped into pattern graph, which is constructed by connecting nodes based on links/relationships required by user. The primary objective of graph pattern matching (GPM) is to determine all the candidates matching to a pattern query in a large data graph. This survey paper discusses various graph pattern matching approaches, used by researchers in recent year for query processing. Further, we also highlight the various challenges imposed by modern day computing and possible future research directions in query processing using graph pattern matching.