DimStiller: Workflows for dimensional analysis and reduction

Abstract

DimStiller is a system for dimensionality reduction and analysis. It frames the task of understanding and transforming input dimensions as a series of analysis steps where users transform data tables by chaining together different techniques, called operators, into pipelines of expressions. The individual operators have controls and views that are linked together based on the structure of the expression. Users interact with the operator controls to tune parameter choices, with immediate visual feedback guiding the exploration of local neighborhoods of the space of possible data tables. DimStiller also provides global guidance for navigating data-table space through expression templates called workflows, which permit re-use of common patterns of analysis.

Visual Soccer Analytics: Understanding the Characteristics of Collective Team Movement Based on Feature-Driven Analysis and Abstraction

“…The rank-by-feature framework [18] suggested a set of comparative views to identify features of high descriptive power and allowed the user to interactively select features. The Dimstiller approach defines a workflow and appropriate views to help users in feature selection and dimensionality reduction [19]. In [20], an approach for the comparison of alternative high-dimensional feature spaces based on 2D projections allowed identifying similarities and differences in candidate features.…”

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

“…[51], [52], [53], [54], [55], [56] Groups & Classification [57] [58], [59] [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74] [75], [76], [77], [78], [79], [80] Dependence & Prediction [81], [82], [46] [83], [84], [85], [86], [87], [88], [89] [90], [91], [92] being analyzed. The results are then presented to the user through different visual encodings that are often accompanied by interaction.…”

“…Such a mechanism enables the analyst to steer the computational resources accordingly to areas where more precision is needed. Ingram et al [54] present a system called DimStiller, where a selection of data transformations are chained together interactively to achieve dimension reduction (see Figure 2). Endert et al [53] introduce observation level interactions to assist computational analysis tools to deliver more reliable results.…”

An Affordance-Based Framework for Human Computation and Human-Computer Collaboration

“…For some tasks such as image labeling [21,33,59,62,73,74], visual search [6,10], and query validation [48,80], the systems presented rely heavily on the users' visual perceptive abilities, with the machine serving only as a facilitator between the human and the data. For other tasks such as exploring high-dimensional datasets [68,83], classification [4,51], and dimension reduction [28,36], machine affordances (which will be discussed at length in Section 5) are combined with human visual processing to achieve superior results.…”

“…The study of general human-computer collaboration offers a plethora of examples of successful human/machine teams [15,23,37,39,41,46,50,65,66,68,83]. Developments in supervised machine learning in the visualization community present several vetted techniques for human intervention into computationally complex tasks [3,4,12,18,28,29,36,47,51,57]. The emerging field of human computation inverts the traditional paradigm of machines providing computational support for problems that humans find challenging, and demonstrates success using aggregated human processing power facilitated by machines to perform difficult computational tasks such as image labeling [21,33,73,74], annotating audio clips [44,49], and even folding proteins [20].…”