Name
Abstract | ||
|---|---|---|
Due to the increasing use of RDF data, efficient processing of SPARQL queries over RDF datasets has become an important issue. However, existing solutions suffer from two limitations: 1) they cannot answer SPARQL queries with wildcards in a scalable manner; and 2) they cannot handle frequent updates in RDF repositories efficiently. Thus, most of them have to reprocess the dataset from scratch. In this paper, we propose a graph-based approach to store and query RDF data. Rather than mapping RDF triples into a relational database as most existing methods do, we store RDF data as a large graph. A SPARQL query is then converted into a corresponding subgraph matching query. In order to speed up query processing, we develop a novel index, together with some effective pruning rules and efficient search algorithms. Our method can answer exact SPARQL queries and queries with wildcards in a uniform manner. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solution. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.14778/2002974.2002976 | PVLDB |
Keywords | Field | DocType |
rdf repository,subgraph matching,sparql query,rdf data,effective pruning rule,exact sparql query,effective maintenance algorithm,rdf triple,query rdf data,rdf datasets,query processing | Data mining,RDF query language,Search algorithm,Relational database,Information retrieval,Computer science,Linked data,SPARQL,RDF/XML,RDF Schema,Database,RDF | Journal |
Volume | Issue | ISSN |
4 | 8 | 2150-8097 |
Citations | PageRank | References |
106 | 2.61 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Zou | 1 | 1161 | 68.43 |
| Jinghui Mo | 2 | 107 | 3.63 |
| Lei Chen | 3 | 6239 | 395.84 |
| M. Tamer Özsu | 4 | 4504 | 582.63 |
| Dongyan Zhao | 5 | 998 | 96.35 |