A Scalable Approach for Context Based Complex Service Discovery

Discovering and matching services is an area that has been extensively explored. In this paper we envision services that advertise not only their functional parameters, but also highlight the Quality of Service(QoS) guarantees (or non-functional parameters) they can provide. As a result users can also incorporate QoS requirements along with the service request. Given the vast pool of services available today, leading to complex ontologies, the search space becomes extremely large, increasing the complexity of the search. We construct an overlay reflecting the relationships between the services, which facilitates the pruning of the entire search space. Additionally our system takes into consideration the user context which provides information pertaining to the users preferences. (eg: A user could be performance-savvy or functionally-cautious etc) We propose an algorithm CCD (Context based Complex service Discovery), which utilizes the inputs provided by the users and determines the similarity quotient for the functional and QoS parameters. Our experiments show that CCD significantly improves the scalability of the search by aggressively pruning the search space, achieved by visiting only relevant nodes. CCD further uses the requester context to improve the recommendations provided to the requester. We also compare CCD with two baseline approaches based on the depth-first search algorithm on a travel ontology, which was created using real service definitions from the Open Travel Alliance (OTA).