Examples - Visualization

A picture says more than a thousand words. This is even more so true for statistics. Visualizing results, generating plots from data is an integral part of ADAMS. Check out what ADAMS can do in the visual department.

Note: the videos should only be considered educational, as some of the concepts in ADAMS have changed over time. E.g., global actors are now called callable, since they can appear in different scopes within the flow. Also, SingleFileSupplier and MultiFileSupplier got merged into the FileSupplier actor.

Learning curve for a WEKA classifier

Generates and visualizes a learning curve for J48, a non-incremental WEKA classifier. The classifier gets trained every 25 instances and evaluated against test set and Accuracy, RMSE and AUC gets plotted in one plot.

../../images/learning_curve.png
  1. Read in dataset incrementally and buffer incoming data
  2. Every 25 instances, build classifier and evaluate against test set (defined as global actor, see 6)
  3. Obtain accuracy and create a plot container for Accuracy plot sequence
  4. Get root mean squared error and turn it into a plot container as well, this time for the RMSE sequence
  5. Retrieve AUC (area under curve) statistics and generate plot container for AUC sequence.
  6. Sequence of actors for reading in a test set
  7. The plot for all three statistics

Visualization of seismic data

Seismic data was retrieved using the Geonet client.

../../images/geonet1.png
  1. Read text file, line by line
  2. Extract Unix timestamp, convert into double and store as variable
  3. Extract amplitude, convert into double and store as variable
  4. Turn timestamp and amplitude into a double array
  5. Generate data structure for plot
  6. The global plot

Email alerts for seismic events

Seismic data was retrieved using the Geonet client. Apart from visualizing the data, this flow also sends out an email alert if a tremor exceeds a certain strength.

../../images/geonet2.png
  1. Read text file, line by line
  2. Extract Unix timestamp, convert into double and store as variable
  3. Convert timestamp into human-readable representation for email
  4. Extract amplitude, convert into double and store as variable
  5. Check whether incoming token (amplitude) is larger than threshold and email hasn't been sent yet
  6. Note that email was sent and send email with information about event (time/date and amplitude)
  7. Turn timestamp and amplitude into a double array
  8. Generate data structure for plot
  9. The global plot

LEIA - visualizing bridge sensor data

Visualizing sensor data of the InfraWatch project using the LEIA/ADAMS framework:

Bridge vibration

Illustration of sensor data processing with the ADAMS/LEIA workflow engine.

Vehicle detection

Demonstration of the ADAMS/LEIA workflow engine on traffic sensor data.

Publication

Knobbe, A., Blockeel, H., Koopman, A., Calders, T., Obladen, B., Bosma, C., Galenkamp, H., Koenders, E., and Kok, J.: InfraWatch: Data Management of Large Systems for Monitoring Infrastructural Performance. IDA Proceedings (2010). [pdf]