Presented by Justin Domke

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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

Motivation

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

Standard time plots are very compelling,

but can only display a limited amount of data

Idea:

Query the data!

Notation

niis an item in a time series data set

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

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

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

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

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)

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