TY - GEN
T1 - FreGraPaD
T2 - 10th IEEE International Conference on Research Challenges in Information Science, IEEE RCIS 2016
AU - Belghaouti, Fethi
AU - Bouzeghoub, Amel
AU - Kazi-Aoul, Zakia
AU - Chiky, Raja
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/23
Y1 - 2016/8/23
N2 - Nowadays, high volumes of data are generated and published at a very high velocity by real-time systems, such as social networks, e-commerce, weather stations and sensors, producing heterogeneous data streams. To take advantage of linked data and offer interoperable solutions, semantic Web technologies have been used. To analyze these huge volumes of data, different stream mining algorithms exist such as compression or load-shedding. Nevertheless, most of them need many passes through the data and often store part of it on disk. If we want to apply efficient compression on semantic data streams, we need to first detect frequent graph patterns in RDF streams. In this article, we present FreGraPaD, an algorithm that detects those patterns in a single pass, using exclusively internal memory and following a data structure oriented approach. Experimental results clearly confirm the good accuracy of FreGraPaD in detecting frequent graph patterns from semantic data streams.
AB - Nowadays, high volumes of data are generated and published at a very high velocity by real-time systems, such as social networks, e-commerce, weather stations and sensors, producing heterogeneous data streams. To take advantage of linked data and offer interoperable solutions, semantic Web technologies have been used. To analyze these huge volumes of data, different stream mining algorithms exist such as compression or load-shedding. Nevertheless, most of them need many passes through the data and often store part of it on disk. If we want to apply efficient compression on semantic data streams, we need to first detect frequent graph patterns in RDF streams. In this article, we present FreGraPaD, an algorithm that detects those patterns in a single pass, using exclusively internal memory and following a data structure oriented approach. Experimental results clearly confirm the good accuracy of FreGraPaD in detecting frequent graph patterns from semantic data streams.
U2 - 10.1109/RCIS.2016.7549333
DO - 10.1109/RCIS.2016.7549333
M3 - Conference contribution
AN - SCOPUS:84987608333
T3 - Proceedings - International Conference on Research Challenges in Information Science
BT - IEEE RCIS 2016 - IEEE 10th International Conference on Research Challenges in Information Science
A2 - Ralyte, Jolita
A2 - Espana, Sergio
A2 - Souveyet, Carine
PB - IEEE Computer Society
Y2 - 1 May 2016 through 3 May 2016
ER -