In this paper, we explore how autocompletion can be used as a data discovery scaffold to
help users with natural language interaction during visual analysis.
This is work I did with my co-authors Enamul, Dae Hyun, and Angel.
Autocompletion is a useful mechanism in user interfaces for displaying in-situ suggestions
to the user as they type.
It is a feature prevalent in various search interfaces on the web, desktop and mobile.
While natural language interfaces for visual analysis are gaining traction, these systems
tend to focus on limited syntactic query autocompletion without any preview of the underlying data.
So, in this paper we explore how autocompletion can be used to help a user discover data while
typing an utterance and we also want to better understand the types of previews that users
find useful in their analytical workflows.
To address this problem, we implement Sneak Pique, as a design probe to explore how autocompletion
can specifically support the user during their analytical workflows.
The system implements a look ahead parser to parse the query.
The autocompletion detection module polls the query as the user types, triggering grammar
parse tree errors while the query is incomplete.
These parse errors are then passed to the autocompletion generator that introspects
on the grammar rules that could make the query syntactically complete.
With the help of the data manager, the data preview updates the autocompletion suggestion
with data frequency information.
The autocompletion suggestion is then displayed in the interface.
A user can then interact with the autocompletion and update their query to generate a visualization
response.
We explore a design space of possible text and widget autocompletion variants to better
understand user preferences for the display of data previews, sort order and navigating
data hierarchies.
In this table, each row shows various text and widget based autocompletion representations,
with DP indicating a data preview of the data frequency values.
Using Sneak Pique as a design probe, we ran three Mechanical Turk experiments to better
understand user preferences of the various autocompletion variants.
The first experiment compared autocompletion variants with and without data frequency displayed.
The second experiment looked at sort order preferences.
The third experiment looked at user preferences for displaying and navigating hierarchical
data.
After running the three experiments we concluded that data previews are useful to show.
Participants preferred sorting by data frequency in the text autocompletion...
and chronological ordering for time data
Histogram sliders were useful for showing both numerical and temporal ranges.
While text autocompletion was the preferred variant for displaying hierarchical data,
participants liked the calendar widget for navigating temporal data.
These takeaways were used to inform the final implementation of Sneak Pique.
We now walk through the interface of Sneak Pique by showing you a demo of how the system
works.
Here is the Sneak Pique interface showing a dashboard of coronavirus cases around the
world -
The user starts out by typing - "show me cases in..."
Map and calendar widgets appear to autocomplete the partial query.
Notice that the data frequency of the values is color encoded in each of the widgets.
The user picks the US and the dashboard gets updated.
The user then types "on", showing a calendar widget with the data preview for the US, which
is in the context of the query.
She finds that there is a high incidence of cases in April and picks a date.
She now wants to see only deaths and picks the text autocompletion to complete her query.
She resets the context by typing "start over"
and types - "cases more than 300 between..."
to show a date range slider.
She then picks a range.
She proceeds to add a country.
Notice that only countries with cases greater than 300 are shown in the map widget.
We conducted an informal user study to collect qualitative feedback from users interacting
with Sneak Pique.
Results showed that autocompletion with data previews can serve as a useful scaffold during
visual analysis tasks.
We identify workflows where autocompletion suggestions in Sneak Pique help users in better
sense-making during query formulation.
We also identify new opportunities for how autocompletion can help with complex previews
and personalization.
And with that, thanks for attending my talk and I’m happy to take any questions or comments.