A Visualization Architecture for Collaborative Analytical and Data Provenance Activities

Abstract

Abstract-When exploring noisy or visually complex data, such as seismic data from the oil and gas industry, it is often the case that algorithms cannot completely identify features of interest. Human intuition must complete the process. Given the nature of intuition, this can be a source of differing interpretations depending on the human expert; thus we do not have a single feature but multiple views of a feature. Managing multi-user and multi-version interpretations, combined with version tracking, is challenging as these interpretations are often stored as geometric objects separately from the raw data and possibly in different local machines. In this paper we combine the storage of the raw data with the storage of the interpretations produced by the visualization of features by multiple user sessions. We present case studies that illustrate our system's ability to reproduce users' amendments to the interpretations of others and the ability to retrace the history of amendments to a visual feature.

Fine-Grained Provenance of Users’ Interpretations in a Collaborative Visualization Architecture

“…The expert interpretation is very central to the definition of the features and the considerations outlined above are of critical importance (Bacon et al, 2003). This paper presents a continuation of a work that we have previously published (Al-Naser et al, 2013a;Al-Naser et al, 2013b); here we present further analysis and evaluation. The contributions of this paper are as follows: 1.…”

NeuroBlocks – Visual Tracking of Segmentation and Proofreading for Large Connectomics Projects

“…More recently, Al-Naser et al [2] employ provenance in the context of seismic visualization to manage multi-user and multi-version seismic interpretations. However, none of these systems have tackled provenance for large segmentation data in complex, distributed workflows.…”