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

Standard time plots are very compelling, but can only display a limited amount of data

Idea: Query the data!

Notation n i is an item in a time series data set n i (t) is the value of n i at time t

Three Widgets: (1) Timebox A timebox is a 4-tuple b = (t min, t max, v min, v max ) n i satisfies b if for all t, t min ≤ t ≤ t max, v min ≤ n i (t) ≤ v max

Three Widgets: (2) Variable Time Timebox A variable time timebox is a 5-tuple b = (t min, t max, v min, v max, R) n i satisfies b if: there exists t 0, t min ≤ t 0 ≤ t max - R, such that for all t, t 0 ≤ t ≤ t 0 +R, v min ≤ n i (t) ≤ v max t min v min v max R

Three Widgets: (3) Angular Query Widget An angular query widget is a 4-tuple b = (t min, t max, θ min, θ max ) n i satisfies b if for all t, t min ≤ t ≤ t max, θ min ≤ φ(n i (t), n i (t)) ≤ θ max Where φ is the angle formed on the graph. min max

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 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

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)

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)

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 , , or 49-99”) –Form fill in fastest –Range sliders second. –Timeboxes last.

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 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?