Direct manipulation interactions on projections are often incorporated in visual analytics applications. These interactions enable analysts to provide incremental feedback to the system in a semi-supervised manner, demonstrating relationships that the analyst wishes to find within the data. However, determining the precise intent of the analyst is a challenge. When an analyst interacts with a projection, the inherent ambiguity of interactions can lead to a variety of possible interpretations that the system can infer. Previous work has demonstrated the utility of clusters as an interaction target to address this “With Respect to What” problem in dimension-reduced projections. However, the introduction of clusters introduces interaction inference challenges as well. In this work, we discuss the interaction space for the simultaneous use of semi-supervised dimension reduction and clustering algorithms. We introduce a novel pipeline representation to disambiguate between interactions on observations and clusters, as well as which underlying model is responding to those analyst interactions. We use a prototype visual analytics tool to demonstrate the effects of these ambiguous interactions, their properties, and the insights that an analyst can glean from each.

Representing branching and comparative analyses in computational notebooks is complicated by the 1-dimensional (1D), top-down list arrangement of cells. Given the ubiquity of these and other non-linear features, their importance to analysis and narrative, and the struggles current 1D computational notebooks have, enabling organization of computational notebook cells in 2 dimensions (2D) may prove valuable. We investigated whether and how users would organize cells in such a "2D Computational Notebook" through a user study and gathered feedback from participants through a follow-up survey and optional interviews. Through the user study, we found 3 main design patterns for arranging notebook cells in 2D: Linear, Multi-Column, and Workboard. Through the survey and interviews, we found that users see potential value in 2D Computational Notebooks for branching and comparative analyses, but the expansion from 1D to 2D may necessitate additional navigational and organizational aids.

How do analysts think about grouping and spatial operations? This overarching research question incorporates a number of points for investigation, including understanding how analysts begin to explore a dataset, the types of grouping/spatial structures created and the operations performed on them, the relationship between grouping and spatial structures, the decisions analysts make when exploring individual observations, and the role of external information. This work contributes the design and results of such a study, in which a group of participants are asked to organize the data contained within an unfamiliar quantitative dataset. We identify several overarching approaches taken by participants to design their organizational space, discuss the interactions performed by the participants, and propose design recommendations to improve the usability of future high-dimensional data exploration tools that make use of grouping (clustering) and spatial (dimension reduction) operations.

Interactive machine learning (ML) systems are difficult to design because of the “Two Black Boxes” problem that exists at the interface between human and machine. Many algorithms that are used in interactive ML systems are black boxes that are presented to users, while the human cognition represents a second black box that can be difficult for the algorithm to interpret. These black boxes create cognitive gaps between the user and the interactive ML model. In this paper, we identify several cognitive gaps that exist in a previously-developed interactive visual analytics (VA) system, Andromeda, but are also representative of common problems in other VA systems. Our goal with this work is to open both black boxes and bridge these cognitive gaps by making usability improvements to the original Andromeda system. These include designing new visual features to help people better understand how Andromeda processes and interacts with data, as well as improving the underlying algorithm so that the system can better implement the intent of the user during the data exploration process. We evaluate our designs through both qualitative and quantitative analysis, and the results confirm that the improved Andromeda system outperforms the original version in a series of high-dimensional data analysis tasks.

Direct manipulation interactions on projections are often incorporated in visual analytics applications. These interactions enable analysts to provide incremental feedback to the system in a semi-supervised manner, demonstrating relationships that the analyst wishes to find within the data. However, determining the precise intent of the analyst is a challenge. When an analyst interacts with a projection, the inherent ambiguity of some interactions leads to a variety of possible interpretations that the system could infer. Previous work has demonstrated the utility of clusters as an interaction target to address this "With Respect to What" problem in dimension-reduced projections. However, the introduction of clusters introduces interaction inference challenges as well. In this work, we discuss the interaction space for the simultaneous use of semi-supervised dimension reduction and clustering algorithms. Within this exploration, we highlight existing interaction challenges of such interactive analytical systems, describe the benefits and drawbacks of introducing clustering, and demonstrate a set of interactions from this space.

Creating an interactive, accurate, and low-latency big data visualisation is challenging due to the volume, variety, and velocity of the data. Visualisation options range from visualising the entire big dataset, which could take a long time and be taxing to the system, to visualising a small subset of the dataset, which could be fast and less taxing to the system but could also lead to a less-beneficial visualisation as a result of information loss. The main research questions investigated by this work are what effect sampling has on visualisation insight and how to provide guidance to users in navigating this trade-off. To investigate these issues, we study an initial case of simple estimation tasks on histogram visualisations of sampled big data, in hopes that these results may generalise. Leveraging sampling, we generate subsets of large datasets and create visualisations for a crowd-sourced study involving a simple cognitive visualisation task. Using the results of this study, we quantify insight, sampling, visualisation, and perception error in comparison to the full dataset. We use these results to model the relationship between sample size and insight error, and we propose the use of our model to guide big data visualisation sampling.

Abstract There is fast‐growing literature on provenance‐related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence‐based guidelines and highlights concrete opportunities for future development in this emerging area. The survey and papers discussed can be explored online interactively at https://provenance-survey.caleydo.org .

Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.

Exploring high-dimensional data is challenging. Dimension reduction algorithms, such as weighted multidimensional scaling, support data exploration by projecting datasets to two dimensions for visualization. These projections can be explored through parametric interaction, tweaking underlying parameterizations, and observation-level interaction, directly interacting with the points within the projection. In this article, we present the results of a controlled usability study determining the differences, advantages, and drawbacks among parametric interaction, observation-level interaction, and their combination. The study assesses both interaction technique effects on domain-specific high-dimensional data analyses performed by non-experts of statistical algorithms. This study is performed using Andromeda, a tool that enables both parametric and observation-level interaction to provide in-depth data exploration. The results indicate that the two forms of interaction serve different, but complementary, purposes in gaining insight through steerable dimension reduction algorithms.

Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms.

BACKGROUND: Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics. RESULTS: We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family. Models are clustered using a custom similarity metric, and a visual interface is provided that allows a researcher to interactively compare the structures of pairs of models as well as view simulation results. CONCLUSIONS: We illustrate the usefulness of MOSBIE via two case studies in the cell signaling domain. We also present feedback provided by domain experts and discuss the benefits, as well as the limitations, of the approach.