© 2010 Artur Dubrawski 1

T-Cube Web Interface in RTBP: A Review of R&D Challenges

Artur Dubrawski, Ph.D, M.Eng.Director, Auton LabSenior Systems Scientist, The Robotics InstituteAdjunct Professor, Heinz College School of Information Systems and ManagementCarnegie Mellon Universityawd@cs.cmu.edu

© 2010 Artur Dubrawski 2

Real-Time Biosurveillance

What it is:

Rapid detection of emerging potentially adverse events in public health data

Plausible approach taken in RTBP:

1. Detect emerging anomalous patterns in data

2. Treat them as potential threats 3. Report them to human users for

further evaluation and response

© 2010 Artur Dubrawski 3

Real-Time Biosurveillance in Practice

Keys to success: 1.Reliable baselines

– We estimate them from historical data– More reliable data more reliable results

2.Use of statistics– We rank detected events according to how

mathematically unusual they appear– We try to do that well, even if data contains some

errors

© 2010 Artur Dubrawski 4

Real-Time Biosurveillance in Practice

Key technical challenges: 1.Size and complexity of data

– It poses computational and interpretational problemsT-Cube is very helpful in addressing such issues

2.Usability of tools– The tools must be tailored to specific needs of their users

In practice, all such needs are not known in advance, and they need to be identified iteratively “as we go”

−The tools should minimize the user’s exposure to complexity of the underlying computations

−And they should support understanding of findings (using e.g. interactive visualization, slicing-and-dicing, etc.)T-Cube Web Interface aims to meet those requirements

© 2010 Artur Dubrawski 5

Bio-surveillance

Interactive analyticsAstrophysics

Food safety

Nuclear threat detection

Learning Locomotion

Safety of agriculture

Fleet prognostics

United Nations CTBTO

×

© 2010 Artur Dubrawski 6

An Example of an Important Improvement Since the Previous Review

Automated, pre-scheduled screening of data for events of routine and fundamental interest – dramatically reduces complexity of the tool– The users do not have to perform complicated

operations to access results of massive screening – they are pre-computed on a daily basis and available upon a single click of a mouse

– The results are browsable and sortable– Details of data leading to alerts are easily accessible

(again, single click of a mouse)

– However, an (improved) interface for ad-hoc analyzes is still available for use

© 2010 Artur Dubrawski 7

Automated Screening

Working with you, we have identified four routine screening scenarios− They can be executed automatically on a regular schedule− Results are one click away

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Automated Screening

− It takes one more mouse click to see the distribution of alert signal on the map

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Automated Screening

− The analyst can then animate these results through time to see if the disease distributes in some specific spatio-temporal way

© 2010 Artur Dubrawski 10

Other Improvements

Multiple-window size temporal scan– Sometimes, the most recent alert is not the most significant

of those that could be issued recently:

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1. Management of data– Correctness of data is a prerequisite for useful results– Apparently, it is hard to maintain consistency of data formats (e.g.

naming of diseases) between subsequent revisions of the database– We should work together on improving those processes– We can probably develop some software tools for error checking

2. Better maps– Show the map together with time series– Nice looking, more interactive maps (e.g. based on Google Maps)

– Trade-off: They may require some extra network bandwidth

3. Better pivot tables – With contents that match report forms used in the current system(s)

– They could be prepared automatically and on pre-defined schedule to meet the current reporting requirements

– Add features: sorting, exporting

4. User training– Tutorial texts and videos, perhaps web-based certification tests

Selected Key Remaining Challenges and Opportunities for Further Improvements