Method for Web service clustering

Abstract

The invention discloses a method for Web service clustering which comprises a Web service library, a main control device and a tag library. The method comprises the following the steps: step 1, using a vector space model (VSM) method to convert Web services into vector sets; step 2, according to an application demand, determining the weight of the Web services; and step 3, using a locality sensitive Hashing (LSH) method to cluster the vector sets. Compared with the prior art, the method provided by the invention has the beneficial effects that: 1, the compatibility with the exiting protocols and technologies is maintained aiming at the clustering for web services description language (WSDL) files; 2. compared with the Kmeans method and the like, the efficiency of the method provided by the invention is very high; and the higher the vector space dimension of the Web service is, the more obvious the high efficiency of the method provided by the invention is; and 3, the Web service clustering result can be used for finding out the Web services and combining the Web services, thus having stronger universality and causing the method provided by the invention to have strong backward compatibility.

Description

technical field [0001] The present invention relates to the field of Web services, especially a method for clustering Web services, which combines Vector Space Model (VSM, vector space model) technology to vectorize Web services, and uses Locality Sensitive Hashing (LSH, location-sensitive Hashing Greek) to expand and complete the clustering of Web services. Background technique [0002] With the continuous development of various Internet technologies and the requirement of collaborative work between heterogeneous platforms, Web services, as an excellent solution, have gradually received more and more attention. Web services can integrate different applications to form a unified solution, which is widely used in e-commerce, workflow and even daily life. A single Web service is difficult to solve complex application requirements. Therefore, the academic and industrial circles pay more attention to Web services, such as Web service composition and Web service discovery. In t...

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Problems solved by technology

The clustering methods used in these studies are mostly the clustering methods in common data mining or the improvement of these methods, such as K-means method, DIANA method, etc. These clustering methods have certain advantages in specific cases, but However, there are the following deficiencies: First, the clustering of Web services is inaccurate, which is caused by the limitations of its clustering method itself, such as K-means is very sensitive to outliers, and a small number of outliers will greatly affect the clustering results. The second is that most clustering methods have low clustering efficiency for high-dimensional spaces (that is, when there are many attributes of Web services), and because they only consider the clustering of Web services based on a certain aspect, such as only for Web services Although it is suitable for web service composition, it does not consider QoS considerations, making it difficult to quickly find web services with high QoS levels when using this clustering result, so it is not universal

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