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Kaskade: Graph Views for Efficient Graph Analytics by da Trindade, Joana M. F.; Karanasos, Konstantinos; Curino, Carlo; Madden, Samuel; Shun, Julian is a scholarly article available to read on EtoBox.

What is Kaskade: Graph Views for Efficient Graph Analytics about?

Graphs are an increasingly popular way to model real-world entities and relationships between them, ranging from social networks to data lineage graphs and biological datasets. Queries over these large graphs often involve expensive subgraph traversals and complex analytical computations. These real-world graphs are often substantially more structured than a generic vertex-and-edge model would suggest, but this insight has remained mostly unexplored by existing graph engines for graph query optimization purposes. Therefore, in this work, we focus on leveraging structural properties of graphs and queries to automatically derive materialized graph views that can dramatically speed up query evaluation. We present KASKADE, the first graph query optimization framework to exploit materialized graph views for query optimization purposes. KASKADE employs a novel constraint-based view enumeration technique that mines constraints from query workloads and graph schemas, and injects them during view enumeration to significantly reduce the search space of views to be considered. Moreover, it introduces a graph view size estimator to pick the most beneficial views to materialize given a query se

Author

da Trindade, Joana M. F.; Karanasos, Konstantinos; Curino, Carlo; Madden, Samuel; Shun, Julian

Published

2019

Language

EN