Paper Info
Title
The Intuitive Power of Graph Pivots For User Exploration and Adaptive Data Abstraction.
Abstract
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 systemu0027s design, as well as its qualitative evaluation with users, clarifies exactly when and how the useru0027s intent in a series of 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 useru0027s intent beyond the current step. They also reveal ways that a series of graph can expose the semantics of the data from the useru0027s 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 useru0027s tasks.
Year
Venue
Field
2018
arXiv: Human-Computer Interaction
Visual complexity,Graph,Abstraction,Computer science,Human–computer interaction,Jacob's Ladder,Semantics
DocType
Volume
Citations 
Journal
abs/1810.03019
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Alex Bigelow1323.87
Megan Monroe200.68