Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
Daniel Klan , Department of Computer Science&Automation, Ilmenau University of Technology, Germany
Katja Hose , Department of Computer Science&Automation, Ilmenau University of Technology, Germany
Marcel Karnstedt , Digital Enterprise Research Institute, National University of Ireland, Galway
Kai-Uwe Sattler , Department of Computer Science&Automation, Ilmenau University of Technology, Germany
Sensor networks have evolved to a powerful infrastructure component for event monitoring in many application scenarios. In addition to simple filter and aggregation operations, an important task in processing sensor data is data mining - the identification of relevant information and patterns. Limited capabilities of sensor nodes in terms of storage and processing capacity, battery lifetime, and communication demand a power-efficient, preferably sensor-local processing. In this paper, we present AnduIN, a system for developing, deploying, and running in-network data mining tasks. The system consists of a data stream processing engine, a library of operators for sensor-local processing, a box-and-arrow editor for specifying data mining tasks and deployment, a GUI providing the user with current information about the network and running queries, and an alerter notifying the user if a better query execution plan is available. At the demonstration site, we plan to show our system in action using burst detection as example application.
Daniel Klan, Katja Hose, Marcel Karnstedt, Kai-Uwe Sattler, "Power-aware data analysis in sensor networks", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 1125-1128, doi:10.1109/ICDE.2010.5447760