A Bloom Filter-Powered Technique Supporting Scalable Semantic Discovery in Data Service Networks

More and more Earth data analytics software products are published onto the Internet as a service, in the format of either heavyweight WSDL service or lightweight RESTful API. Such reusable data analytics services form a data service network, which allows Earth scientists to compose (mashup) services into value-added ones. Therefore, it is important to have a technique that is capable of helping Earth scientists quickly identify appropriate candidate datasets and services in the global data service network. Most existing services discovery techniques, however, mainly rely on syntax or semantics-based service matchmaking between service requests and available services. Since the scale of the data service network is increasing rapidly, the run-time computational cost will soon become a bottleneck. To address this issue, this project presents a way of applying network routing mechanism to facilitate data service discovery in a service network, featuring scalability and performance. Earth data services are automatically annotated in Web Ontology Language for Services (OWL-S) based on their metadata, semantic information, and usage history. Deterministic Annealing (DA) technique is applied to dynamically organize annotated data services into a hierarchical network, where virtual routers are created to represent semantic local network featuring leading terms. Afterwards Bloom Filters are generated over virtual routers. A data service search request is transformed into a network routing problem in order to quickly locate candidate services through network hierarchy. A neural network-powered technique is applied to assure network address encoding and routing performance. A series of empirical study has been conducted to evaluate the applicability and effectiveness of the proposed approach.