Putting lipstick on pig

TLDR

Workflow provenance traditionally treats modules as black boxes, assuming outputs depend on all inputs and ignoring internal state, yet in practice outputs often rely on only a subset of inputs and the module’s internal state. The authors propose a provenance framework that combines database- and workflow-style provenance by exposing module functionality with Pig Latin to capture internal state and fine-grained dependencies. The framework uses a novel provenance graph that models module invocations, provides a compact representation of fine-grained provenance, supports zooming and what‑if queries, and is implemented in the Lipstick system with a benchmark for evaluation. The evaluation shows that fine-grained workflow provenance can be tracked and queried effectively.

Abstract

Workflow provenance typically assumes that each module is a "black-box", so that each output depends on all inputs ( coarse-grained dependencies). Furthermore, it does not model the internal state of a module, which can change between repeated executions. In practice, however, an output may depend on only a small subset of the inputs ( fine-grained dependencies) as well as on the internal state of the module. We present a novel provenance framework that marries database-style and workflow-style provenance, by using Pig Latin to expose the functionality of modules, thus capturing internal state and fine-grained dependencies. A critical ingredient in our solution is the use of a novel form of provenance graph that models module invocations and yields a compact representation of fine-grained workflow provenance. It also enables a number of novel graph transformation operations, allowing to choose the desired level of granularity in provenance querying (ZoomIn and ZoomOut), and supporting "what-if" workflow analytic queries. We implemented our approach in the Lipstick system and developed a benchmark in support of a systematic performance evaluation. Our results demonstrate the feasibility of tracking and querying fine-grained workflow provenance.