Name
Abstract | ||
|---|---|---|
Although the emergence of SPARQL endpoints that allow end-users and applications to query the RDF data they want, continuous processing of building a very large query over diverse SPARQL endpoints requires a sophisticated method. However, current RDF Stream Processing (RSP) applications are limited in terms of scalability and administrative autonomy, due to their tight-coupled data sources (e.g., RDF streams) and being unable to coordinate with existing SPARQL engines. In this paper, we propose a novel continous query processing that is equipped with a proactive adaptation for enhancing a planbased policy, pulling RDF data periodically from remote sources. Our proactive adaptation forecasts the future update pattern of a source, and decides the best action that guarantees the improved data freshness and efficient system workload. We verify the proposed approach in terms of data adaptability, detection latency, and transmission cost in distributed settings. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/WI-IAT.2015.168 | WI-IAT |
Keywords | Field | DocType |
RDF Stream Processing,SPARQL Endpoints,Continuous query processing,the federation of SPARQL endpoints | Data mining,RDF query language,Information retrieval,Computer science,SPARQL,Schedule,Named graph,Web service,RDF Schema,Database,RDF,Scalability | Conference |
Volume | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sejin Chun | 1 | 21 | 3.56 |
| Seungmin Seo | 2 | 24 | 5.68 |
| Won Woo Ro | 3 | 197 | 27.94 |
| Kyong-Ho Lee | 4 | 13 | 3.23 |