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Scientific Workflows are abstractions used to model in silico scientific experiments. Cloud environments are still incipient in collecting and recording prospective and retrospective provenance. This paper presents an approach to support collecting metadata provenance of in silico scientific experiments executed in public clouds. The strategy was implemented as a distributed and modular architecture named Matriohska. This paper also presents a provenance data model compatible with PROV specification. We also show preliminary results that describe how provenance metadata was captured from the components running in the cloud.

Business Provenance provides important documentation that is an essential to increase the trustworthiness and traceability of end-to-end business operations. This paper presents two data marts that allows multidimensional analysis of business provenance metadata collected from a real e-business scenario. Provenance was collected with the aid of an architecture named BizProv. We conclude the paper with the identification of the challenges that will drive future research of BizProv.