ORDEN: outlier region detection and exploration in sensor networks

ABSTRACT

Sensor networks play a central role in applications that monitor variables in geographic areas such as the traffic volume on roads or the temperature in the environment. A key feature users are often interested in when employing such systems is the detection of unusual phenomena, that is, anomalous values measured by the sensors. In this demonstration, we present a system, called ORDEN, that allows for the detection and (visual) exploration of outliers and anomalous events in sensor networks in real-time. In particular, the system constructs outlier regions from anomalous sensor measurements to provide for a comprehensive description of the spatial extent of phenomena of interest. With our system, users can interactively explore displayed outlier regions and investigate the heterogeneity within individual regions using different parameter and threshold settings. Using real-world sensor data streams from different application domains, we demonstrate the effectiveness and utility of our system.

REFERENCES

• 1: California Irrigation Management Information System (CIMIS). http://wwwcimis.water.ca.gov.
• 2: MIT Computer Science and Artificial Intelligence Lab: Intel lab sensor data. http://db.csail.mit.edu/labdata/labdata.html.
• 3: Tropical Atmosphere Ocean Project. http://www.pmel.noaa.gov/tao/.
• 4: Conny Franke , Michael Gertz, Detection and Exploration of Outlier Regions in Sensor Data Streams, Proceedings of the 2008 IEEE International Conference on Data Mining Workshops, p.375-384, December 15-19, 2008 [doi>10.1109/ICDMW.2008.21]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Spatial databases and GIS
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)

General Terms:
Algorithms, Experimentation, Management

Keywords:
outlier detection, sensor data, stream processing