Sensor Mining at work: Principles and a Water Quality Case-Study
by Christos Faloutsos (SCS, CMU) and Jeanne VanBriesen (CEE, CMU)
How can we find patterns in a collection of measurements,
say, on water quality sensors?
What do these patterns tell us?
Is the water safe to drink? Are we under biological attack?
How many sensors do we need to place, and where,
to answer these questions in real time?
The instructors have been collaborating on exactly these problems
for the past several years.
The tutorial will report on these experiences.
Specifically, the tutorial surveys the related areas and has two goals:
(a) to review the main principles and main data mining tools
for sensor data analysis (correlation discovery, SVD, ICA, Fourier, Wavelets)
(b) to showcase them on a real, important application,
namely monitoring the quality of drinking water.
The tutorial ends with a list of future directions for data mining research,
motivated by the water quality monitoring application.