Extracting causal graphs from an open provenance data model

The open provenance architecture approach to the challenge was distinct in several regards. In particular, it allows different components of the challenge workflow to independently record documentation, and for the workflow to be executed in different environments, made possible by an open, well-defined data model and architecture. Another noticeable feature is that we distinguish between the data recorded about what has occurred, process documentation, and the provenance of a data item, which is all that caused the data item to be as it is. In this view, provenance is obtained as the result of a query over process documentation. This distinction allows us to tailor the system to best address the separate requirements of recording and querying documentation. Other notable features include the explicit recording of causal relationships between both events and data items, an interaction-based world model, intensional definition of data items in queries rather than relying on explicit naming mechanisms, and styling of documentation to support non-functional application requirements such as reducing storage costs or ensuring privacy of data. In this paper, we describe how each of these features aid us in answering the challenge's provenance queries.