TY - JOUR
T1 - Proactive replication of dynamic linked data for scalable RDF stream processing
AU - Chun, Sejin
AU - Jung, Jooik
AU - Jin, Xiongnan
AU - Yoon, Seungjun
AU - Lee, Kyong Ho
PY - 2016
Y1 - 2016
N2 - In this paper, we propose a scalable method of proactively replicating a subset of remote datasets for RDF Stream Processing. Our solution achieves a fast query processing by maintaining the replicated data up-to-date before query evaluation. To construct the replication process effectively, we present an update estimation model to handle the changes in updates over time. With the update estimation model, we re-construct the replication process in response to the outdated data. Finally, we conduct exhaustive tests with a real-world dataset to verify our solution.
AB - In this paper, we propose a scalable method of proactively replicating a subset of remote datasets for RDF Stream Processing. Our solution achieves a fast query processing by maintaining the replicated data up-to-date before query evaluation. To construct the replication process effectively, we present an update estimation model to handle the changes in updates over time. With the update estimation model, we re-construct the replication process in response to the outdated data. Finally, we conduct exhaustive tests with a real-world dataset to verify our solution.
UR - http://www.scopus.com/inward/record.url?scp=84992364345&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992364345&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:84992364345
SN - 1613-0073
VL - 1690
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2016 Posters and Demonstrations Track, ISWC P and D 2016
Y2 - 19 October 2016
ER -