Visualizing Flow of Uncertainty through Analytical Processes

Abstract

Uncertainty can arise in any stage of a visual analytics process, especially in data-intensive applications with a sequence of data transformations. Additionally, throughout the process of multidimensional, multivariate data analysis, uncertainty due to data transformation and integration may split, merge, increase, or decrease. This dynamic characteristic along with other features of uncertainty pose a great challenge to effective uncertainty-aware visualization. This paper presents a new framework for modeling uncertainty and characterizing the evolution of the uncertainty information through analytical processes. Based on the framework, we have designed a visual metaphor called uncertainty flow to visually and intuitively summarize how uncertainty information propagates over the whole analysis pipeline. Our system allows analysts to interact with and analyze the uncertainty information at different levels of detail. Three experiments were conducted to demonstrate the effectiveness and intuitiveness of our design.

Visualization System Requirements for Data Processing Pipeline Design and Optimization

“…Each colored cell indicates a requirement that is important for a given case study/paper, and an 'x' indicates that the requirement remains unmet by users' current tools. [25] req req req req req Chemical process models [26] x req req x Economic modelling [27] req req xx req req x req Aircraft engine design [28] req req x req req Phylogenetic trees [29,30] x req x x req req Molecular evolution [31,32] x x req x req x * Raidou et al [75] iCoCooN req req req * Ruppert et al [46] --x req x Luboshik et al [38] --x req req req Bruckner et al [10] --x req x req * Beham et al [43] Cupid req req xx Konyha et al [39] --x req x Pretorius et al [12] Paramorama req x req Afzal et al [36] RVF x req req x Bergner et al [13] ParaGlide xx req Torsney-Weir et al [33] Tuner x req x x * Padua et al [11] --x req x * Bögl et al [42] TiMoVA req req req x x req Spence et al [73] --req req Berger et al [9] --req x xx Piringer et al [40] HyperMoVal xx req req req Matkovic et al [17] --req x Coffey et al [34] --x x req Matkovic et al [50] --x Potter et al [49] Ensemble-Vis x req Booshehrian et al [19] Vismon req x req x Brecheisen et al [52] --x req x Unger et al [54] --req req req Amirkhanov et al [74] --x Marks et al [48] Design Galleries req x Waser et al [18] World Lines req x x * Martins et al [35] --x req x * Wu [53] --xx x Guo et al [41] --req…”

“…4.5), stochastic algorithms result in uncertain results that require detailed inspection by the expert. Despite the frequency with which sensitivity and uncertainty were highlighted as important in an application (see Table 1), it is rare that they appear together as requirements (exceptions are [9], [19], [38], [40], [52], [53] and the molecular evolution case study).…”

“…[9] and the phylogenetic trees case study in Sec. 4.7), to enable interactive refinement of parameters to optimize outputs (e.g., [12], [40], [46]), to assess the effect of inputs on outputs ('understanding the black box' [38]) or to interactively analyze the influence of input uncertainty on the output uncertainty through the pipeline [53]. All these cases require that users have the possibility to interactively assess subjective outputs in the context of input parameters.…”

“…For example, users can only assess sensitivity for a subset of parameter combinations [9], and want to check the parameter settings against diverse inputs in 3D image segmentation or compare the outputs of molecular evolution hypotheses. Users also want to understand uncertainty 'stack-up' over the pipeline steps [52], [53] and need innovative visualizations to understand the interplay of simulation parameters and outcomes [18]. In multi-step pipelines (see Fig.…”

Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration

“…In general, most of these techniques focus on 1D or 2D datasets and cannot be readily extended to multidimensional datasets. Wu et al [29] introduced the standard error ellipsoid to characterize uncertainty arising in any stage of a visual analytic process for multidimensional datasets. In addition, evaluations of different uncertainty visualization techniques have attracted much attention recently.…”