PivotSlice

Contributors

Jian Zhao, Christopher Collins, Fanny Chevalier, and Ravin Balakrishnan

Abstract

Many datasets, such as scientific literature collections, contain multiple heterogeneous facets which derive implicit relations, as well as explicit relational references between data items. The exploration of this data is challenging not only because of large data scales but also the complexity of resource structures and semantics. In this paper, we present PivotSlice, an interactive visualization technique which provides efficient faceted browsing as well as flexible capabilities to discover data relationships. With the metaphor of direct manipulation, PivotSlice allows the user to visually and logically construct a series of dynamic queries over the data, based on a multi-focus and multi-scale tabular view that subdivides the entire dataset into several meaningful parts with customized semantics. PivotSlice further facilitates the visual exploration and sensemaking process through features including live search and integration of online data, graphical interaction histories and smoothly animated visual state transitions. We evaluated PivotSlice through a qualitative lab study with university researchers and report the findings from our observations and interviews. We also demonstrate the effectiveness of PivotSlice using a scenario of exploring a repository of information visualization literature.

Publications

  • J. Zhao, C. Collins, F. Chevalier, and R. Balakrishnan, “Interactive Exploration of Implicit and Explicit Relations in Faceted Datasets,” IEEE Trans. on Visualization and Computer Graphics (Proc. of the IEEE Conf. on Visual Analytics Science and Technology (VAST)), vol. 19, iss. 12, pp. 2080-2089, 2013.
    [Bibtex] [PDF] [DOI]
    @Article{zha2013a,
      author = {Jian Zhao and Christopher Collins and Fanny Chevalier and Ravin Balakrishnan},
      title = {Interactive Exploration of Implicit and Explicit Relations in Faceted Datasets},
      journal = {IEEE Trans. on Visualization and Computer Graphics (Proc. of the IEEE Conf. on Visual Analytics Science and Technology (VAST))},
      volume = 19, 
      number = 12,
      month = dec, 
      year = 2013,
      pages = {2080 - 2089},
      doi = {10.1109/TVCG.2013.167}
    }

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Acknowledgements

Research

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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

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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

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Reading Comprehension on Mobile Devices

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

Optimizing Hierarchical Visualizations with the Minimum Description Length Principle

Lexichrome

SentimentState: Exploring Sentiment Analysis on Twitter

Facilitating Discourse Analysis with Interactive Visualization

DimpVis

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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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