Piatkowski/2012a: iST-MRF: Interactive Spatio-Temporal Probabilistic Models for Sensor Networks

Bibtype Inproceedings
Bibkey Piatkowski/2012a
Author Piatkowski, Nico
Ls8autor Piatkowski, Nico
Title {iST-MRF}: Interactive Spatio-Temporal Probabilistic Models for Sensor Networks
Booktitle International Workshop at ECML PKDD 2012 on Instant Interactive Data Mining (IID)
Abstract Streams of sensor measurements arise from twitter, mobile
phone networks, internet traffic, road traffic, home automation systems,
seismic motion and sea level - to mention just a few. The interactive
exploration and modelling of such measurements from multiple sensors
induces the need for algorithms that are capable of processing the data
as it becomes available and that can quickly provide partial results based
on the data seen so far. Beside these requirements, the algorithm should
capture the inherent spatio-temporal dependency structure within sen-
sor data and allow a predictive analysis on arbitrary subsets of sensors.
Spatio-Temporal Markov Random Fields (ST-MRF) are known to meet
the requirements of modelling the dynamics of sensor networks. ST-MRF
tracks the empirical distribution of each sensor and concurrently updates
a Maximum Likelihood estimate of the underlying distribution. In the
first part of this paper, we show how to train such models in an online
fashion in order to perform near-instant updates to the model and pro-
vide them to the user. In the second part, we present iST-MRF, a free
open source software for interactive modelling and analyzing data from
sensor networks, which implements and visualizes ST-MRF. It guaran-
tees high performance computations for offline models and concurrent
learning and prediction for online models. We present two exemplary
applications of iST-MRF to sensor network data, namely modelling a
network of temperature sensors and a location prediction task.
Year 2012
Projekt SFB876-A1