Sketching allows analysts to specify complex and free-form patterns of interest. Visual query systems can make use of sketches to locate these patterns of interest in large datasets. However, sketching is ambiguous: the same drawing could represent a multitude of potential queries. In this work, we investigate these ambiguities as they apply to visual query systems for time series data. We define a class of “invariants” — the properties of a time series that the analyst wishes to ignore when performing a sketch-based query. We present the results of a crowd-sourced study, showing that these invariants are key components of how people rate the strength of match between sketch and target. We adapt a number of algorithms for time series matching to support invariants in sketches. Lastly, we present a web-deployed prototype sketch-based visual query system that relies on these invariants. We apply the prototype to example datasets from finance, the digital humanities, and political science.

@InProceedings{CG16,
  author       = "Correll, Michael and Gleicher, Michael",
  title        = "The Semantics of Sketch: A Visual Query System for Time Series Data",
  booktitle    = "Proceedings of the 2016 IEEE Conference on Visual Analytics Science and Technology (VAST)",
  month        = "oct",
  year         = "2016",
  publisher    = "IEEE",
  note         = "To appear",
  projecturl   = "https://github.com/uwgraphics/SketchQuery",
  url          = "http://graphics.cs.wisc.edu/Papers/2016/CG16"
}