Name
Abstract | ||
|---|---|---|
In recent years, the scale of RDF datasets is increasing rapidly, the query research on RDF datasets in the transitional centralized environment is unable to meet the increasing demand of data query field, especially the top-k query. Based on the Spark distributed computing system and the HBase distributed storage system, a novel method is proposed for top-k query. A top-k query plan STA Spark Threshold Algorithm is proposed to reduce the connection operation of RDF data. Furthermore, a better algorithm SSJA Spark Simple Join Algorithm is presented to reduce the sorting related operations for the intermediate data. A cache mechanism is also proposed to speed up the SSJA algorithm. The experimental results show that the SSJA algorithm performs better than the STA algorithm in term of the cost and applicability, and it can significantly improve the SSJA's performance by introducing the cache mechanism. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.4018/IJWSR.2017070105 | Int. J. Web Service Res. |
Keywords | Field | DocType |
Distributed Optimization, RDF Datasets, Spark, Top-k Query | Data mining,Spark (mathematics),Computer science,Cache,Distributed data store,Sort-merge join,Sorting,RDF Schema,RDF,Query plan | Journal |
Volume | Issue | ISSN |
14 | 3 | 1545-7362 |
Citations | PageRank | References |
1 | 0.37 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| jinguang gu | 1 | 46 | 13.50 |
| Hao Dong | 2 | 8 | 5.01 |
| Zhao Liu | 3 | 25 | 10.73 |
| Fangfang Xu | 4 | 1 | 1.05 |