Dynamic query tools for time series data sets: Timebox widgets for interactive exploration Harry Hochheiser Ben Shneiderman

1
Dynamic query tools for time series data
setsTimebox widgets for interactive
explorationHarry HochheiserBen Shneiderman

• Presented by Justin Domke

2
Motivation

• Data that changes over time is common.
• Algorithmic and statistical methods are good at
  answering questions.
• How to choose the questions themselves?

3
Standard time plots are very compelling, but can
only display a limited amount of data
4
Idea Query the data!
5
Notation
ni is an item in a time series data set ni(t)
is the value of ni at time t
6
Three Widgets (1) Timebox
A timebox is a 4-tuple b (tmin, tmax, vmin,
vmax) ni satisfies b if for all t, tmin t
tmax, vmin ni(t) vmax
7
Three Widgets (2) Variable Time Timebox
A variable time timebox is a 5-tuple b (tmin,
tmax, vmin, vmax,R) ni satisfies b if there
exists t0, tmin t0 tmax- R, such that for all
t, t0 t t0R, vmin ni(t) vmax
vmax
vmin
tmin
tmin
R
8
Three Widgets (3) Angular Query Widget
An angular query widget is a 4-tuple b (tmin,
tmax, ?min, ?max) ni satisfies b if for all t,
tmin t tmax, ?min f(ni(t), ni(t))
?max Where f is the angle formed on the graph.
max
min
9
Demonstration

• Standard Timeboxes
• Drag From Display Window
• Manpulate multiple boxes
• Coupling of windows
• Variable Time Timeboxes
• Angular Queries
• Query Inversion
• Query Multiple Variables
• Leaders and Laggards

10
Performance

• Over 75 of time is spent on query evaluation.
• Naïve approach
• For each item in the set, examine every point in
  each timebox.
• Easy improvement
• Throw an item out if it fails any query.

11
Performance (2) Alternatives

• Suppose data has n time series, each with m time
  points.
• Think of this as mn points in 2-d space.
• Use geometric methods to find the points in each
  given range.
• Increment a value for each point in a series. If
  the sum is right, the series satisfies the query.
• Use orthogonal range tree or grid approach with
  buckets

12
Performance 3
Seq Sequential Orth Orthogonal Range
Tree Grid-X Grid approach w/ X buckets
Average query completion time vs. number of items
for random data. (100 time points)
13
Performance 4
Seq Sequential Orth Orthogonal Range
Tree Grid-X Grid approach w/ X buckets
Average query completion time vs. number of time
points for random data. (100 items)
14
Design Studies

• 24 Computer Science students completed various
  tasks using different but semantically equivalent
  input mechanisms
• Timebox queries
• Fill-in
• Range sliders

15
Design Study 1

• Fully specified tasks. (During days 22-23, are
  there more stocks between 69-119, 59-109, or
  49-99)
• Form fill in fastest
• Range sliders second.
• Timeboxes last.

16
Design Study 2

• More open-ended tasks.
• Comare
• Timeboxes with graphical output
• Forms with graphical output
• Forms with tabular output
• No statistically significant difference.

(Were the users already familiar with timeboxes?)
17
Comments

• Problems with user interface?
• Why timesearcher, instead of parallelcoordinate
  searcher?
• In the performance experiment, what did the data
  look like?
• In the design study, were the users already
  familiar with Timesearcher?