TY - GEN
T1 - Graph-oriented load-shedding for semantic Data Stream processing
AU - Belghaouti, Fethi
AU - Bouzeghoub, Amel
AU - Aoul, Zakia Kazi
AU - Chiky, Raja
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/3
Y1 - 2015/12/3
N2 - The continuous and progressive growth of the need for knowledge extraction from continuous data streams, in an exponential way, has favored the emergence of a new research axis from the semantic web community. In the few last years, many semantic data stream processing systems have been proposed by combining Data Stream Management Systems (DSMS) technologies and Semantic Web technologies (RDF1/SPARQL2) for annotation, publication and reasoning on these data streams. However, considering their infinite volume and unknown velocity, processing and storing their contents remain impossible, which leads to introduce techniques for reducing load and/or summarizing data. In this context, we propose a graph-oriented approach to reduce the semantic data streams volume. In order to validate our approach, we implemented it using Simple Random Sampling and Stratified Random Sampling and we experimented it using the CSRBench benchmark. Our approach allows to maintain the data consistency and their semantic level.
AB - The continuous and progressive growth of the need for knowledge extraction from continuous data streams, in an exponential way, has favored the emergence of a new research axis from the semantic web community. In the few last years, many semantic data stream processing systems have been proposed by combining Data Stream Management Systems (DSMS) technologies and Semantic Web technologies (RDF1/SPARQL2) for annotation, publication and reasoning on these data streams. However, considering their infinite volume and unknown velocity, processing and storing their contents remain impossible, which leads to introduce techniques for reducing load and/or summarizing data. In this context, we propose a graph-oriented approach to reduce the semantic data streams volume. In order to validate our approach, we implemented it using Simple Random Sampling and Stratified Random Sampling and we experimented it using the CSRBench benchmark. Our approach allows to maintain the data consistency and their semantic level.
KW - Big Data
KW - Load-Shedding
KW - Sampling
KW - Semantic Data Stream
KW - Semantic Web
UR - https://www.scopus.com/pages/publications/84962799506
U2 - 10.1109/IWCIM.2015.7347064
DO - 10.1109/IWCIM.2015.7347064
M3 - Conference contribution
AN - SCOPUS:84962799506
T3 - 2015 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2015
BT - 2015 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2015
Y2 - 29 October 2015 through 30 October 2015
ER -