RDF-3X: a RISC-style engine for RDF

By Thomas Neumann, Gerhard Weikum. Proceedings of the VLDB Endowment, 2008

652 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7da8b094a558ec9265f37393fc80bd26039dff51

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Citations in the corpus

AMIE: association rule mining under incomplete evidence in ontological knowledge bases (2013, methodology): “Hence, efficient SPARQL engines such as RDF3X [29] are an alternative option.”
Foundations of SPARQL query optimization (2008, methodology): “Tackling the specific challenges of the SPARQL query language, over the last years various proposals for the efficient evaluation of SPARQL have been made, comprising a wide range of optimization techniques such as normal forms [26], triple pattern reordering based on selectivity estimations [20, 23”
The RDF-3X engine for scalable management of RDF data (2010, methodology): “The current paper extends this prior publication in two major ways: (1) we describe the algorithms for query compilation and query optimization, which are merely sketched in [39]; (2) we present a novel way of efficiently supporting updates to RDF-3X databases, and give performance measurements for ”
gStore: Answering SPARQL Queries via Subgraph Matching (2011, methodology): “In this section, we evaluate our method over two real large RDF datasets, and compare it with SW-store [1], RDF-3x [12], and x-RDF-3x [15].”
Binary RDF representation for publication and exchange (HDT) (2013, background): “The presence of star and chaining nodes has been also described in data and queries (star- and chain-shaped join queries) [43,44].”
A Distributed Graph Engine for Web Scale RDF Data (2013, background): “In this section, we discuss the performance implications of the two different approaches.”
The Case for Learned Index Structures (2017, background): “Since B+-trees consume significant memory, there has also been a lot of work in compressing indexes, such as prefix/suffix truncation, dictionary compression, key normalization [33, 36, 55], or hybrid hot/cold indexes [75].”