RDF Data Storage Techniques for Efficient SPARQL Query Processing Using Distributed Computation Engines

2018 ◽  
Author(s):  
Mahmudul Hassan ◽  
Srividya K. Bansal
Keyword(s):  
Query Processing ◽  
Data Storage ◽  
Distributed Computation ◽  
Sparql Query ◽  
Rdf Data

Map-Side Join Processing of SPARQL Queries Based on Abstract RDF Data Filtering

2019 ◽  
Vol 30 (1) ◽  
pp. 22-40 ◽  
Author(s):  
Minjae Song ◽  
Hyunsuk Oh ◽  
Seungmin Seo ◽  
Kyong-Ho Lee
Keyword(s):  
Query Processing ◽  
Input Data ◽  
General Trend ◽  
Processing System ◽  
Sparql Query ◽  
Data Filtering ◽  
Mapreduce Framework ◽  
Query Plan ◽  
Network Bandwidth ◽  
Rdf Data

The amount of RDF data being published on the Web is increasing at a massive rate. MapReduce-based distributed frameworks have become the general trend in processing SPARQL queries against RDF data. Currently, query processing systems that use MapReduce have not been able to keep up with the increase of semantic annotated data, resulting in non-interactive SPARQL query processing. The principal reason is that intermediate query results from join operations in a MapReduce framework are so massive that they consume all available network bandwidth. In this article, the authors present an efficient SPARQL processing system that uses MapReduce and HBase. The system runs a job optimized query plan using their proposed abstract RDF data to decrease the number of jobs and also decrease the amount of input data. The authors also present an efficient algorithm of using Map-side joins while also using the abstract RDF data to filter out unneeded RDF data. Experimental results show that the proposed approach demonstrates better performance when processing queries with a large amount of input data than those found in previous works.

RDF Data Storage and Query Processing Schemes

2018 ◽  
Vol 51 (4) ◽  
pp. 1-36 ◽  
Author(s):  
Marcin Wylot ◽  
Manfred Hauswirth ◽  
Philippe Cudré-Mauroux ◽  
Sherif Sakr
Keyword(s):  
Query Processing ◽  
Data Storage ◽  
Rdf Data

Towards efficient SPARQL query processing on RDF data

2010 ◽  
Vol 15 (6) ◽  
pp. 613-622 ◽  
Author(s):  
Chang Liu ◽  
Haofen Wang ◽  
Yong Yu ◽  
Linhao Xu
Keyword(s):  
Query Processing ◽  
Sparql Query ◽  
Rdf Data

scholarly journals An empirical evaluation of cost-based federated SPARQL query processing engines

Semantic Web ◽  
2021 ◽  
pp. 1-26
Author(s):  
Umair Qudus ◽  
Muhammad Saleem ◽  
Axel-Cyrille Ngonga Ngomo ◽  
Young-Koo Lee
Keyword(s):  
Query Processing ◽  
Detailed Analysis ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Sparql Query ◽  
Evaluation Metrics ◽  
Future Cost ◽  
Query Plan ◽  
Fine Grained ◽  
Runtime Performance

Finding a good query plan is key to the optimization of query runtime. This holds in particular for cost-based federation engines, which make use of cardinality estimations to achieve this goal. A number of studies compare SPARQL federation engines across different performance metrics, including query runtime, result set completeness and correctness, number of sources selected and number of requests sent. Albeit informative, these metrics are generic and unable to quantify and evaluate the accuracy of the cardinality estimators of cost-based federation engines. To thoroughly evaluate cost-based federation engines, the effect of estimated cardinality errors on the overall query runtime performance must be measured. In this paper, we address this challenge by presenting novel evaluation metrics targeted at a fine-grained benchmarking of cost-based federated SPARQL query engines. We evaluate five cost-based federated SPARQL query engines using existing as well as novel evaluation metrics by using LargeRDFBench queries. Our results provide a detailed analysis of the experimental outcomes that reveal novel insights, useful for the development of future cost-based federated SPARQL query processing engines.

scholarly journals IDSM ChemWebRDF: SPARQLing small-molecule datasets

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jakub Galgonek ◽  
Jiří Vondrášek
Keyword(s):  
Data Storage ◽  
Small Molecule ◽  
Web Application ◽  
Query Language ◽  
Data Interoperability ◽  
Sparql Endpoint ◽  
Data Source ◽  
Rdf Data ◽  
Relational Form ◽  
Federated Queries

AbstractThe Resource Description Framework (RDF), together with well-defined ontologies, significantly increases data interoperability and usability. The SPARQL query language was introduced to retrieve requested RDF data and to explore links between them. Among other useful features, SPARQL supports federated queries that combine multiple independent data source endpoints. This allows users to obtain insights that are not possible using only a single data source. Owing to all of these useful features, many biological and chemical databases present their data in RDF, and support SPARQL querying. In our project, we primary focused on PubChem, ChEMBL and ChEBI small-molecule datasets. These datasets are already being exported to RDF by their creators. However, none of them has an official and currently supported SPARQL endpoint. This omission makes it difficult to construct complex or federated queries that could access all of the datasets, thus underutilising the main advantage of the availability of RDF data. Our goal is to address this gap by integrating the datasets into one database called the Integrated Database of Small Molecules (IDSM) that will be accessible through a SPARQL endpoint. Beyond that, we will also focus on increasing mutual interoperability of the datasets. To realise the endpoint, we decided to implement an in-house developed SPARQL engine based on the PostgreSQL relational database for data storage. In our approach, data are stored in the traditional relational form, and the SPARQL engine translates incoming SPARQL queries into equivalent SQL queries. An important feature of the engine is that it optimises the resulting SQL queries. Together with optimisations performed by PostgreSQL, this allows efficient evaluations of SPARQL queries. The endpoint provides not only querying in the dataset, but also the compound substructure and similarity search supported by our Sachem project. Although the endpoint is accessible from an internet browser, it is mainly intended to be used for programmatic access by other services, for example as a part of federated queries. For regular users, we offer a rich web application called ChemWebRDF using the endpoint. The application is publicly available at https://idsm.elixir-czech.cz/chemweb/.

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