Name
Abstract | ||
|---|---|---|
Motivated by the ongoing success of Linked Data and the growing amount of semantic data sources available on theWeb, new challenges to query processing are emerging. Especially in distributed settings that require joining data provided by multiple sources, sophisticated optimization techniques are necessary for efficient query processing. We propose novel join processing and grouping techniques to minimize the number of remote requests, and develop an effective solution for source selection in the absence of preprocessed metadata. We present FedX, a practical framework that enables efficient SPARQL query processing on heterogeneous, virtually integrated Linked Data sources. In experiments, we demonstrate the practicability and efficiency of our framework on a set of real-world queries and data sources from the Linked Open Data cloud. With FedX we achieve a significant improvement in query performance over state-of-the-art federated query engines. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25073-6_38 | International Semantic Web Conference (1) |
Keywords | Field | DocType |
optimization technique,efficient sparql query processing,query performance,efficient query processing,federated query processing,semantic data source,state-of-the-art federated query engine,linked data source,linked data,linked open data cloud,data source,real-world query | Query optimization,Web search query,Data mining,Query language,RDF query language,Query expansion,Information retrieval,Computer science,Sargable,Web query classification,SPARQL,Database | Conference |
Volume | ISSN | Citations |
7031 | 0302-9743 | 153 |
PageRank | References | Authors |
6.46 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Schwarte | 1 | 303 | 15.59 |
| Peter Haase | 2 | 1727 | 114.59 |
| Katja Hose | 3 | 1011 | 69.52 |
| Ralf Schenkel | 4 | 1641 | 114.11 |
| Michael Schmidt | 5 | 353 | 19.30 |