Service Discovery Based on Objective and Subjective Measures

Web services have become a primary mechanism for consuming resources available on the Internet. As more and more services are published on the Web, automated service discovery is critical to consumers to identify relevant and reliable services efficiently. In this paper, we enhance the Web Service Crawler Engine (WSCE) framework by introducing comparison measures to allow for more accurate identification, discovery and ranking of relevant Web services. To discover services effectively, we need to be able to measure and compare the similarity among services. Most ontology-based and IR-based discovery techniques assume that service input/output are simple data types when calculating service similarity. However, real-world services published on the Web usually have complex data types input/output parameters. Furthermore, a good match of parameters does not guarantee good usability and good reliability. The relevant services must be further evaluated by users' past experiences, based on both objective and subjective measures, to make optimal solution selection possible. This paper proposes a service matchmaking algorithm that considers the complex data types of service input/output parameters, as well as experience-based objective and subjective measures for ranking. Experiments show that our approach performs better than previous works that only consider simple data types.