TY - GEN
T1 - Semantic Web Service Composition in Big Data Environment
AU - Huang, Jun
AU - Zhou, Yide
AU - Duan, Qiang
AU - Xing, Cong Cong
PY - 2017/7/1
Y1 - 2017/7/1
N2 - The widespread deployment of web services and rapid development of big data applications bring in new challenges to web service compositions in the context of big data. The large number of web services processing a huge amount of diverse data together with the complex and dynamic relationships among the services require automatic composition of semantic web services to be performed quickly, thereby demanding more efficient service composition algorithms. In this paper, we investigate the issue of web service composition in big data environments by proposing novel composition algorithms with low time-complexity. Specifically, we decompose the service composition into three stages - construction of parameter expansion graphs, transformation of service dependence graphs, and backtracking search for service compositions. Based on the parameter expansion strategies, we then propose two efficient semantic web service composition algorithms and analyze their time complexity. We also conduct comparison experimentally to evaluate the efficiency of the algorithms and validate their effectiveness using a big data (service composition) set.
AB - The widespread deployment of web services and rapid development of big data applications bring in new challenges to web service compositions in the context of big data. The large number of web services processing a huge amount of diverse data together with the complex and dynamic relationships among the services require automatic composition of semantic web services to be performed quickly, thereby demanding more efficient service composition algorithms. In this paper, we investigate the issue of web service composition in big data environments by proposing novel composition algorithms with low time-complexity. Specifically, we decompose the service composition into three stages - construction of parameter expansion graphs, transformation of service dependence graphs, and backtracking search for service compositions. Based on the parameter expansion strategies, we then propose two efficient semantic web service composition algorithms and analyze their time complexity. We also conduct comparison experimentally to evaluate the efficiency of the algorithms and validate their effectiveness using a big data (service composition) set.
UR - http://www.scopus.com/inward/record.url?scp=85046379399&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046379399&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2017.8254425
DO - 10.1109/GLOCOM.2017.8254425
M3 - Conference contribution
T3 - 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
SP - 1
EP - 7
BT - 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Global Communications Conference, GLOBECOM 2017
Y2 - 4 December 2017 through 8 December 2017
ER -