Answering graph pattern query using incremental views
Komal Singh, Vikram Singh · 2016
In recent years, modeling data in graph structure became evident and effective for processing in some of the prominent application areas like social analytics, health care analytics, scientific analytics etc. The key sources of massively scaled data are petascale simulations, experimental devices, the internet and scientific applications. Hence, there is a demand for adapt graph querying techniques on such large graph data. Graphs are pervasive in large scale analytics, facing the new challenge such as data size, heterogeneity, uncertainty and data quality. Traditional graph pattern matching approaches are based on inherent isomorphism and simulation. In real life applications, many of them either fail to capture structural or semantic or both similarities. Moreover, in real life applications data graphs constantly bear modifications with small updates. In response to these challenges, we propose a notion that revises traditional notions to characterize graph pattern matching using graph views. Based on this characterization, we outline an approach that efficiently solve graph pattern queries problem over both static and dynamic real life data graphs.