Fine-grained provenance of users' interpretations in a collaborative visualization architecture

In this paper, we address the interpretation of seismic imaging datasets from the oil and gas industry—a process that requires expert knowledge to identify features of interest. This is a subjective process as it is based on human expertise and thus it often results in multiple views and interpretations of a feature in a collaborative environment. Managing multi-user and multi-version interpretations, combined with version tracking, is challenging; this is supported by a recent survey that we present in this paper. We address this challenge via a data-centric visualization architecture, which combines the storage of the raw data with the storage of the interpretations produced by the visualization of features by multiple user sessions. Our architecture features a fine-grained data-oriented provenance, which is not available in current methods for visual analysis of seismic data. We present case studies that present the use of our system by geoscientists to illustrate its ability to reproduce users' inputs and amendments to the interpretations of others and the ability to retrace the history of changes to a visual feature.

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