A meta-provenance service to infer context from provenance data of distributed entities

Provenance management has become an integral part of many large-scale distributed computing systems. Tracking the history of data and its usage has led to better understanding of system requirements as well as user needs. Still, the need for an intelligent service that matches the system requirements with user needs is not satisfied. We propose a meta-provenance service that infers context from the provenance information of distributed entities and uses this contextual information to satisfy user needs. We describe our meta-provenance framework by way of describing its implementation in the Calder system. The Calder streaming system enables dynamic invocation of forecast models in LEAD by using a distributed mesh of data mining agents. The meta-provenance service enables sophisticated mapping of user queries from the LEAD portal down to the set of few data mining agents that execute them. Also our meta-provenance service can work at multiple levels of contextual granularity.