Trustworthy provenance framework for document workflow provenance

Abstract

Document workflows, which plays a major role in enterprise business process automation, are dynamic and prone to be changed over time. Assuring the provenance of these workflows is important when comes to quality, long term preservation, forensics and regulatory compliance. This research introduces a Provenance Framework for collecting workflow provenance data, storing them in documents as metadata securely and querying stored provenance data. The author uses the concept of making data objects independent and consider provenance data as intrinsic property of the data object, to support long term preservation of documents with provenance and to maintain the link between the data object and its provenance in cloud over time. Provenance data modeling and representation is done according to the W3C PROV Model. XMP framework is used to store and query provenance data as metadata in documents. Document signatures and metadata encryption is used to ensure security of provenance data.