TY - GEN
T1 - A query based graph-oriented load-shedding for RDF stream processing
AU - Belghaouti, Fethi
AU - Bouzeghoub, Amel
AU - Aoul, Zakia Kazi
AU - Chiky, Raja
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/28
Y1 - 2016/12/28
N2 - To cope with heterogeneity of data in streams, Semantic Web technologies (RDFVSPARQL2) have recently been used for annotation, publication and reasoning on these data. To deal with this new kind of streams, researchers have proposed new systems named RDF Stream Processing (RSP). Unfortunately, in limited system resources environment, these systems are fallible as soon as their maximum supported speed is reached. To overcome these problems, some efforts have been done in this area. Most of them, based on a triple-oriented approach and according to a probabilistic method, decrease the volume of RDF data stream using load-shedding techniques. In this paper we propose an enhancement of a Graph-Oriented approach for load-shedding semantic data streams, by considering the continuous query as input. Conducted experiments show that we can keep the RSP's recall at 100% even if we drop more than half of data.
AB - To cope with heterogeneity of data in streams, Semantic Web technologies (RDFVSPARQL2) have recently been used for annotation, publication and reasoning on these data. To deal with this new kind of streams, researchers have proposed new systems named RDF Stream Processing (RSP). Unfortunately, in limited system resources environment, these systems are fallible as soon as their maximum supported speed is reached. To overcome these problems, some efforts have been done in this area. Most of them, based on a triple-oriented approach and according to a probabilistic method, decrease the volume of RDF data stream using load-shedding techniques. In this paper we propose an enhancement of a Graph-Oriented approach for load-shedding semantic data streams, by considering the continuous query as input. Conducted experiments show that we can keep the RSP's recall at 100% even if we drop more than half of data.
KW - Big Data
KW - Load-Shedding
KW - Sampling
KW - Semantic Data Stream
KW - Semantic Web
UR - https://www.scopus.com/pages/publications/85010952043
U2 - 10.1109/IWCIM.2016.7801184
DO - 10.1109/IWCIM.2016.7801184
M3 - Conference contribution
AN - SCOPUS:85010952043
T3 - 2016 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2016
BT - 2016 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2016
Y2 - 27 October 2016 through 28 October 2016
ER -