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Provenance-Based Refresh in Data-Oriented Workflows

Ikeda, Robert and Salihoglu, Semih and Widom, Jennifer (2011) Provenance-Based Refresh in Data-Oriented Workflows. Technical Report. Stanford InfoLab. (Publication Note: Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM '11), Glasgow, Scotland)




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. 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 experimental results on the overhead of provenance capture, and on the crossover point between selective refresh and full workflow recomputation.

Item Type:Techreport (Technical Report)
ID Code:962
Deposited By:Robert Ikeda
Deposited On:09 Mar 2010 17:28
Last Modified:21 Oct 2011 17:20

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