Utilizing Provenance in Reusable Research Objects

TL;DR

This paper explores how provenance information can enhance the sharing and reuse of research objects by enabling accurate reproduction, partial reuse, and modification, supported by new summarization methods.

Contribution

It introduces two provenance summarization techniques that improve understanding and reuse of research objects, facilitating computational reproducibility and partial reuse.

Findings

Provenance-based methods enable accurate reproduction of experiments.

Summarization techniques improve understanding of research object contents.

Algorithms are shown to be effective and efficient through experiments.

Abstract

Science is conducted collaboratively, often requiring the sharing of knowledge about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs). An experiment, however, seldom includes only datasets, but more often includes software, its past execution, provenance, and associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While a necessary method, mere aggregation is not sufficient for the sharing of computational experiments. Other users must be able to easily recompute on these shared research objects. Computational provenance is often the key to enable such reuse. In this paper, we show how reusable research objects can utilize provenance to correctly repeat a previous reference execution, to construct a subset of a research object for partial reuse, and to reuse existing contents of a research object for modified reuse. We describe two methods to summarize provenance that aid in understanding the contents and past executions of a research object. The first method obtains a process-view by collapsing low-level system information, and the second method obtains a summary graph by grouping related nodes and edges with the goal to obtain a graph view similar to application workflow. Through detailed experiments, we show the efficacy and efficiency of our algorithms.