Provenance-based refresh in data-oriented workflows

Abstract

We consider a general workflow setting in which input data sets are processed by a graph of transformations to produce output results. Our goal is to perform efficient selective refresh of elements in the output data, i.e., compute the latest values of specific output elements when the input data may have changed. We explore how data provenance can be used to enable efficient refresh. Our approach is based on capturing one-level data provenance at each transformation when the workflow is run initially. Then at refresh time provenance is used to determine (transitively) which input elements are responsible for given output elements, and the workflow is rerun only on that portion of the data needed for refresh. Our contributions are to formalize the problem setting and the problem itself, to specify properties of transformations and provenance that are required for efficient refresh, and to provide algorithms that apply to a wide class of transformations and workflows. We have built a prototype system supporting the features and algorithms presented in the paper. We report preliminary experimental results on the overhead of provenance capture, and on the crossover point between selective refresh and full workflow recomputation.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 23 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.