Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00009
Gaëlle Richer, Romain Bourqui, D. Auber
In the context of multiple views, coordination is essential to navigate and grasp the relationships lying behind the different juxtaposed views. Linked highlighting is a typical example of coordination where a subset of the data points is emphasized simultaneously on all views. The strength of this approach is that the selected data can be studied within its context. Other approaches have been used to implement coordination such as using varying levels of transparency or visual links. We propose to use spatial distortion to contribute a similar effect in multiple views. It is particularly suited to the context of multiple views since it alleviates the lack of screen space by reallocating it based on a certain definition of user interest. The proposed method targets coordination between views that represent the same entities and readily adapts to various visualization forms. It is based on a user degree-of-interest function, defined on these entities, that acts as a common ground for the distortion of all views. Views are distorted such that empty areas and areas holding entities of lesser interest are compressed to the benefit of areas holding entities of higher interest. To demonstrate its feasibility and versatility, we describe how to technically apply our approach to several common visualization techniques.
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Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00025
Yuyu Yan, Y. Tao, Sichen Jin, Jin Xu, Hai Lin
Text labeling for classification is a time-consuming and unintuitive process. Given an unannotated text collection, it is difficult for users to determine what label to create and how to label the initial training set for classification. Thus, we present an interactive visual analytics system for incremental text classification based on a semi-supervised topic modeling method, modified Gibbs sampling maximum entropy discrimination latent Dirichlet allocation (Gibbs MedLDA). Given a text collection, Gibbs MedLDA generates topics as a summary of the text collection. We design a scatter plot to display documents and topics simultaneously to show the topic information, and this helps users explore the text collection structurally and find labels for creating. After labeling documents, Gibbs MedLDA is applied to the text collection with labels again, and it generates both the topic and classification information. We also provide a scatter plot with the classifier boundary and a matrix view to present weights of classifiers. Users can iteratively label documents to refine each classifier. We evaluate our system via a user study with a benchmark corpus for text classification and case studies with two unannotated text collections.
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Pub Date : 2018-10-06DOI: 10.1109/PacificVis.2019.00014
Alex Bigelow, M. Monroe
This paper reports on a simple visual technique that boils extracting a subgraph down to two operations—pivots and filters—that is agnostic to both the data abstraction, and its visual complexity scales independent of the size of the graph. The system's design, as well as its qualitative evaluation with users, clarifies exactly when and how the user's intent in a series of pivots is ambiguous—and, more usefully, when it is not. Reflections on our results show how, in the event of an ambiguous case, this innately practical operation could be further extended into "smart pivots" that anticipate the user's intent beyond the current step. They also reveal ways that a series of graph pivots can expose the semantics of the data from the user's perspective, and how this information could be leveraged to create adaptive data abstractions that do not rely as heavily on a system designer to create a comprehensive abstraction that anticipates all the user's tasks.
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