Distributed Top-K Join Queries Optimizing for RDF Datasets

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.