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Workflow provenance typically assumes that each module is a "black-box", sothat each output depends on all inputs (coarse-grained dependencies).Furthermore, it does not model the internal state of a module, which can changebetween repeated executions. In practice, however, an output may depend on onlya small subset of the inputs (fine-grained dependencies) as well as on theinternal state of the module. We present a novel provenance framework thatmarries database-style and workflow-style provenance, by using Pig Latin toexpose the functionality of modules, thus capturing internal state andfine-grained dependencies. A critical ingredient in our solution is the use ofa novel form of provenance graph that models module invocations and yields acompact representation of fine-grained workflow provenance. It also enables anumber of novel graph transformation operations, allowing to choose the desiredlevel of granularity in provenance querying (ZoomIn and ZoomOut), andsupporting "what-if" workflow analytic queries. We implemented our approach inthe Lipstick system and developed a benchmark in support of a systematicperformance evaluation. Our results demonstrate the feasibility of tracking andquerying fine-grained workflow provenance.

Putting Lipstick on Pig: Enabling Database-style Workflow Provenance