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# Presented by Justin Domke - PowerPoint PPT Presentation

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

Presented by Justin Domke

• Data that changes over time is common.

• Algorithmic and statistical methods are good at answering questions.

• How to choose the questions themselves?

but can only display a limited amount of data

Query the data!

niis an item in a time series data set

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

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

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

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

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

• Standard Timeboxes

• Drag From Display Window

• Manpulate multiple boxes

• Coupling of windows

• Variable Time Timeboxes

• Angular Queries

• Query Inversion

• Query Multiple Variables

• 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.

• 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

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)

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)

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

• Timebox queries

• Fill-in

• Range sliders

• 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.

• Comare:

• Timeboxes with graphical output

• Forms with graphical output

• Forms with tabular output

• No statistically significant difference.

(Were the users already familiar with timeboxes?)

• 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?