Scalable SPARQL querying of large RDF graphs

By Jiewen Huang, Daniel J. Abadi, Kun Ren. Proceedings of the VLDB Endowment, 2011

511 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

From "Think Like a Vertex" to "Think Like a Graph" (2013, methodology): “GraphLab [14] and the work in [10], also proposed to employ the Metis [12] (sequential) or the ParMetis [11] (parallel) graph partitioning tools to generate better partitions.”
A Distributed Graph Engine for Web Scale RDF Data (2013, methodology): “Recently, several distributed RDF systems, such as SHARD [29], YARS2 [17], Virtuoso [15], and [20], have been introduced.”
One Trillion Edges: Graph Processing at Facebook-Scale (2015, methodology): “Several DSL graph languages have been built, including Green-Marl [25], which can compile to Giraph code, and SPARQL [26], a graph traversal language.”
WTF: the who to follow service at Twitter (2013, background): “Despite much work and progress (e.g., [12] [11] [26] [4], just to name a few) and the existence of parallel graph partitioning tools such as ParMETIS [12], it remains a very difficult problem [18], especially in the case of large, dynamically changing graphs.”
Understandable Big Data: A survey (2015, background): “Another relevant work in this challenge focuses on efficient RDF repositories partitioning and scalability of SPARQL queries [85].”