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Data lineage describes the relationship between individual input and output data items of a workflow and is an integral ingredient for both traditional (e.g., debugging or auditing) and emergent (e.g., explanations or cleaning) applications. The core, long-standing problem that lineage systems need to address---and the main focus of this paper---is to quickly capture lineage across a workflow in order to speed up future queries over lineage. Current lineage systems, however, either incur high lineage capture overheads, high lineage query processing costs, or both. In response, developers resort to manual implementations of applications that, in principal, can be expressed and optimized in lineage terms. This paper describes S moke , an in-memory database engine that provides both fast lineage capture and lineage query processing. To do so, S moke tightly integrates the lineage capture logic into physical database operators; stores lineage in efficient lineage representations; and employs optimizations if future lineage queries are known up-front. Our experiments on microbenchmarks and realistic workloads show that S moke reduces the lineage capture overhead and lineage query costs by multiple orders of magnitude as compared to state-of-the-art alternatives. On real-world applications, we show that S moke meets the latency requirements of interactive visualizations (e.g., < 150ms) and outperforms hand-written implementations of data profiling primitives.

We highlight the connections between data provenance and interactive visualizations. To do so, we first incrementally add interactions to a visualization and show how these interactions are readily expressible in terms of provenance. We then describe how an interactive visualization system that natively supports provenance can be easily extended with novel interactions.

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