Provenance is a key metadata for assessing electronic documents trustworthiness. It gives an indicator on the reliability and the quality of the document content. Most of the applications exchanging and processing documents on the web or in the cloud become provenance aware and provide heterogeneous, decentralized and not interoperable provenance data. Most of provenance management systems are either dedicated to a specific application (workflow, database) or a specific data type. Those systems were not conceived to support provenance over distributed and heterogeneous sources. This implies that end-users are faced with different provenance models and different query languages. For these reasons, modeling, collecting and querying provenance across heterogeneous distributed sources is still considered as a challenging task. This work presents a new provenance management system (PMS) based on semantic web technologies. It allows to import provenance sources, to enrich them semantically to obtain high level representation of provenance. It supports semantic correlation between different provenance sources and allows the use of a high level semantic query language. In the context of cloud infrastructure where most of applications will be deployed in a near future, scalability is a major issue for provenance management systems. We described here an implementation of our PMS based on an NoSQL database management system coupled with the map-reduce parallel model and show that it scales linearly depending on the size of the processed logs.