Provenance Query Patterns for Many-Task Scientific Computing.

Abstract

Provenance information enable the analysis of large scale many-task computations often specified as scientific workflows. They allow for one to determine how each resulting data set was derived from other data sets and applications. In this work, we survey queries used for exploring provenance information about many-task computations. We present a set of patterns that can be identified in these queries, which is being used as a basis for the design and implementation of a provenance management system for many-task scientific computations, integrated to the Swift parallel scripting system. It has a data model similar to the Open Provenance Model, with extensions that enrich core structural provenance data, represented as consumption and production relationships between applications and data sets, with information about the runtime behavior of each application, and domain-specific information such as the scientific parameters used by applications.

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