TY - GEN
T1 - A matching algorithm for selecting web services based on non-functional features
AU - Elnaffar, Said
PY - 2008
Y1 - 2008
N2 - Searching for a Web service that meets the user requirements can be a complex task especially when the system starts to scale up by increasing the number of Web services, w, in the UDDI registry and by enlarging the number of QoS features (f) by which each Web service is described. This can be perceived as the commonly known nearest neighbor search problem, which typically imposes a time or storage complexity that is exponential in f. In this work, we present a new algorithm (wsSVD) that is founded on the algebraic matrix operation called Singular Value Decomposition (SVD). The basic idea is to encode the features of each Web service by a single value using the SVD. When a user seeks a Web service based on some specific requirements, these requirements get encoded by a single value using the same algorithm, and the matching process takes place in order to find the closest Web service that fulfills the user requirements. Our experiments show that the wsSVD algorithm performs and scales up well in comparison with other matching algorithms.
AB - Searching for a Web service that meets the user requirements can be a complex task especially when the system starts to scale up by increasing the number of Web services, w, in the UDDI registry and by enlarging the number of QoS features (f) by which each Web service is described. This can be perceived as the commonly known nearest neighbor search problem, which typically imposes a time or storage complexity that is exponential in f. In this work, we present a new algorithm (wsSVD) that is founded on the algebraic matrix operation called Singular Value Decomposition (SVD). The basic idea is to encode the features of each Web service by a single value using the SVD. When a user seeks a Web service based on some specific requirements, these requirements get encoded by a single value using the same algorithm, and the matching process takes place in order to find the closest Web service that fulfills the user requirements. Our experiments show that the wsSVD algorithm performs and scales up well in comparison with other matching algorithms.
KW - Nearest neighbor search
KW - Qos
KW - Search algorithm
KW - Singular value decomposition
KW - Svd
KW - Uddi
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M3 - Conference contribution
AN - SCOPUS:84870274159
SN - 9781605609539
T3 - 14th Americas Conference on Information Systems, AMCIS 2008
SP - 2915
EP - 2925
BT - 14th Americas Conference on Information Systems, AMCIS 2008
T2 - 14th Americas Conference on Information Systems, AMCIS 2008
Y2 - 14 August 2008 through 17 August 2008
ER -