Guidance in the human–machine analytics process

Contributors

Christopher Collins, Natalia Andrienko, Tobias Schreck, Jing Yang, Jaegul Choo, Ulrich Engelke, Amit Jena, and Tim Dwyer

Abstract

In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making.

Acknowledgments

This paper is the direct result of an NII Shonan Meeting at the Shonan Village Center in Japan. We acknowledge the hospitality of the Center in making this research possible. This work was partly supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), [grant RGPIN-2015-03916], the Fraunhofer Cluster of Excellence on ‘‘Cognitive Internet Technologies’’ and by the EU through project Track&Know (grant agreement 780754).

Publications

  • C. Collins, N. Andrienko, T. Schreck, J. Yang, J. Choo, U. Engelke, A. Jena, and T. Dwyer, “Guidance in the human–machine analytics process,” Visual Informatics, 2018.
    [Bibtex] [PDF]
    @Article{col2018b,
      author = {Christopher Collins and Natalia Andrienko and Tobias Schreck and Jing Yang and Jaegul Choo and Ulrich Engelke and Amit Jena and Tim Dwyer},
      journal = {Visual Informatics},
      publisher = {Elsevier B.V.},
      title = {{Guidance in the human–machine analytics process}},
      year = {2018},
    }

Research

Guidance in the human–machine analytics process

H-Matrix: Hierarchical Matrix for Visual Analysis of Cross-Linguistic Features in Large Learner Corpora

A Visual Analytics Framework for Adversarial Text Generation

Design by Immersion: A Transdisciplinary Approach to Problem-Driven Visualizations

Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

Discriminability Tests for Visualization Effectiveness and Scalability

Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

ActiveInk: (Th)Inking with Data

Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution

ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics

Detecting Negative Emotion for Mixed Initiative Visual Analytics

EduApps – Supporting Non-Native English Speakers to Overcome Language Transfer Effects

Metatation: Annotation as Implicit Interaction to Bridge Close and Distant Reading

DataTours: A Data Narratives Framework

Perceptual Biases in Font Size as a Data Encoding

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Abbreviating Text Labels on Demand

NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

PhysioEx: Visual Analysis of Physiological Event Streams

Using Visual Analytics of Heart Rate Variation to Aid in Diagnostics

Off-Screen Desktop

PivotSlice

Reading Comprehension on Mobile Devices

#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

Balancing Clutter and Information in Large Hierarchical Visualizations

Lexichrome

SentimentState: Exploring Sentiment Analysis on Twitter

Facilitating Discourse Analysis with Interactive Visualization

DimpVis

Glidgets

TandemTable

Simple Multi-Touch Toolkit

Exploring Text Entities with Descriptive Non-photorealistic Rendering

Investigating the Semantic Patterns of Passwords

Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations

Parallel Tag Clouds to Explore Faceted Text Corpora

VisLink: Revealing Relationships Amongst Visualizations

DocuBurst: Visualizing Document Content using Language Structure

Tabletop Text Entry Techniques

Lattice Uncertainty Visualization: Understanding Machine Translation and Speech Recognition

WordNet Visualization

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