Efficient Service Substitutions with Behavior-Based Similarity Metrics

We define a formal model for information services that incorporates the concept of service similarity. The model places services in metric spaces, and allows for services that have arbitrarily complex inputs and output domains. We then address the challenge of service substitution: finding the services most similar to a given service among a group, possibly large, of candidate services. To solve this nearest neighbor problem efficiently we embed the space of services into a vector space and search for the nearest neighbors in the target space. We report on an extensive experiment that validates both our formalization of similarity and the methods used for finding service substitutions.