This is the first release of PROV-O-Matic as presented at the Big Future of Data event in October 2014.
PROV-O-Matic does the following:
- It wraps Python functions and methods using a decorator, that builds an RDF PROV-O representation of the inputs and outputs of the respective function. The provenance trace is persistent within a Python session. And,
- it integrates provenance tracing in IPython Notebook, a tool frequently used by scientists for analysing data, and reporting on it. All functions defined in the notebook are automatically decorated, and all executions of steps in the notebook are recorded as well (including changing variable values). And
- it integrates a PROV-O-Viz instance for interactive visualization of the provenance graph, and integrates it into IPython notebook.
- Existing provenance traces can be loaded into the notebook, and PROV entities can be revived as Python variables. Use and manipulation of these new variables, will build a provenance trace that connects to the previous trace.