Dynamic query tools for time series data sets:
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Dynamic query tools for time series data sets: Timebox widgets for interactive exploration Harry Hochheiser Ben Shneiderman. Presented by Justin Domke. Motivation. Data that changes over time is common. Algorithmic and statistical methods are good at answering questions.

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Presented by justin domke

Dynamic query tools for time series data sets:Timebox widgets for interactive explorationHarry HochheiserBen Shneiderman

Presented by Justin Domke


Presented by justin domke

Motivation

  • Data that changes over time is common.

  • Algorithmic and statistical methods are good at answering questions.

  • How to choose the questions themselves?


Presented by justin domke

Standard time plots are very compelling,

but can only display a limited amount of data


Presented by justin domke

Idea:

Query the data!


Notation
Notation

niis an item in a time series data set

ni(t) is the value of ni at time t


Presented by justin domke

Three Widgets: (1) Timebox

A timebox is a 4-tuple b = (tmin, tmax, vmin, vmax)

nisatisfiesb if for all t, tmin ≤ t ≤ tmax, vmin≤ ni(t) ≤ vmax


Presented by justin domke

Three Widgets: (2) Variable Time Timebox

A variable time timebox is a 5-tuple b = (tmin, tmax, vmin, vmax,R)

nisatisfiesb if:

there exists t0, tmin ≤ t0 ≤ tmax- R, such that

for all t, t0 ≤ t ≤ t0+R, vmin≤ ni(t) ≤ vmax

vmax

vmin

tmin

tmin

R


Presented by justin domke

Three Widgets: (3) Angular Query Widget

An angular query widget is a 4-tuple b = (tmin, tmax, θmin, θmax)

nisatisfiesb if for all t, tmin ≤ t ≤ tmax, θmin≤ φ(ni(t), ni(t)) ≤ θmax

Where φ is the angle formed on the graph.

max

min


Demonstration
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


Performance
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.


Performance 2 alternatives
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


Performance 3

Seq – Sequential

Orth – Orthogonal Range Tree

Grid-X – Grid approach w/ X buckets

Performance – 3

Average query completion time vs. number of items for random data.

(100 time points)


Performance 4

Seq – Sequential

Orth – Orthogonal Range Tree

Grid-X – Grid approach w/ X buckets

Performance – 4

Average query completion time vs. number of time points for random data.

(100 items)


Design studies
Design Studies

  • 24 Computer Science students completed various tasks using different but semantically equivalent input mechanisms:

    • Timebox queries

    • Fill-in

    • Range sliders


Design study 1
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.


Design study 2
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?)


Comments
Comments

  • Problems with user interface?

  • Why “timesearcher”, instead of “parallelcoordinatesearcher”?

  • In the performance experiment, what did the data look like?

  • In the design study, were the users already familiar with Timesearcher?