An Experimental Comparison of RDF Data Management Approaches in a SPARQL Benchmark Scenario

@inproceedings{Schmidt2008AnEC,
  title={An Experimental Comparison of RDF Data Management Approaches in a SPARQL Benchmark Scenario},
  author={Michael Schmidt and Thomas Hornung and Norbert K{\"u}chlin and Georg Lausen and Christoph Pinkel},
  booktitle={SEMWEB},
  year={2008}
}
Efficient RDF data management is one of the cornerstones in realizing the Semantic Web vision. In the past, different RDF storage strategies have been proposed, ranging from simple triple stores to more advanced techniques like clustering or vertical partitioning on the predicates. We present an experimental comparison of existing storage strategies on top of the SP2Bench SPARQL performance benchmark suite and put the results into context by comparing them to a purely relational model of the… 
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94 Citations
Highly Influential Citations
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Background Citations
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Methods Citations
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94 Citations

SW-Store: a vertically partitioned DBMS for Semantic Web data management
TLDR
The results show that a vertically partitioned schema achieves similar performance to the property table technique while being much simpler to design, and the architecture of SW-Store, a new DBMS that implements these techniques to achieve high performance RDF data management.
Efficient querying of multidimensional RDF data with aggregates: Comparing NoSQL, RDF and relational data stores
Benchmarking Spark-SQL under Alliterative RDF Relational Storage Backends
TLDR
A systematic comparison of there relevant RDF relational schemas queried using Apache Spark shows many interesting insights about the impact of the relational encoding scheme, storage backends and storage formats on the performance of the query execution process.
An In-depth Investigation of Large-scale RDF Relational Schema Optimizations Using Spark-SQL
TLDR
One of the most significant challenges of large-scale RDF data processing over Apache Spark, the relational schema optimization is discussed and insights into these schemas’ relative strengths are provided by comparing three different partitioning techniques and four other storage formats.
Compressed vertical partitioning for efficient RDF management
TLDR
This article introduces a novel RDF indexing technique that supports efficient SPARQL solution in compressed space and enhances this model with two compact indexes listing the predicates related to each different subject and object in the dataset, in order to address the specific weaknesses of vertically partitioned representations.
Towards making sense of Spark-SQL performance for processing vast distributed RDF datasets
TLDR
A systematic evaluation of the performance of SparkSQL engine for processing SPARQL queries using three relevant RDF relational schemas, and two different storage backends, namely, Hive, and HDFS is presented.
FlexTable: Using a Dynamic Relation Model to Store RDF Data
TLDR
This paper proposes a system called FlexTable, where all triples of an instance are coalesced into one tuple and all tuples are stored in relation schemas, based on a lattice structure to automatically evolve schemas while new triples are inserted.
NoSQL Databases for RDF: An Empirical Evaluation
TLDR
This work is the first systematic attempt at characterizing and comparing NoSQL stores for RDF processing and compares their key characteristics when running standard RDF benchmarks on a popular cloud infrastructure using both single-machine and distributed deployments.
Scalable and Efficient Self-Join Processing technique in RDF data
TLDR
An alternative solution to facilitate flexibility and efficiency in that queries and try to reach to the optimal solution to decrease the self-joins as much as possible, this solution based on the idea of "Recursive Mapping of Twin Tables".
Compressed Vertical Partitioning for Efficient RDF Management 1
TLDR
A novel RDF indexing technique that supports efficient SPARQL solution in compressed space that achieves by far the most compressed representations, but also achieves the best overall performance for RDF retrieval in the authors' experimental setup.
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