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DREAM: Distributed RDF Engine with Adaptive Query Planner and Minimal Communication

Summary: DREAM partitions SPARQL queries, not RDF data, avoiding intermediate shuffles and enabling pattern matching. An adaptive, graph-based, rule-oriented planner and a new cost model distribute work across clusters and outperform popular RDF systems. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11177
Venue
VLDB
Year
2015
Pagerank
5.1981241e-05
Overall Rank
6,127 | 57.38%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
2,450 Functional Dependencies for Graphs 2016 SIGMOD 8.7882979e-05
7,818 A Survey and Experimental Comparison of Distributed SPARQL Engines for Very Large RDF Data 2017 VLDB 4.6434716e-05
7,905 S2RDF: RDF Querying with SPARQL on Spark 2016 VLDB 4.6211706e-05
11,738 Stylus: A Strongly-Typed Store for Serving Massive RDF Data 2018 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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