Data integration is in general guided by queries that specify the data required by an application, a user or a community of users. Given the evolution of technology, in later years queries can be issued from devices with different constraints and results are expected to be consumed in different conditions (energy consumption, network bandwidth consumption, economic cost, privacy, trust, and criticality). These quality aspects related to data and to the conditions in which queries and data are evaluated are integrated and associated to the requirements specification through contracts and user profiles. They guide the process in which data is integrated to respond to queries and they significantly increase the complexity of this process.

On the other hand, data integration quality suffers from the lack of control on the data sources implied in the integration.Exploiting the data sources provenance and the trust level of the data provider should improve the results of the integration. Thus, a new mechanism that derives data quality and the trust level is needed.

To this end, our work aims at: (i) studying the SLA models in cloud computing and thus defining an SLA for data integration, and (ii) Proposing an approach for an intelligent trust-based data integration platform on a multi-cloud environment guided by SLA.