ORDEN: outlier region detection and exploration in sensor networks
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Modeling and prediction of moving region trajectories
Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
DBOD-DS: distance based outlier detection for data
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
AnyOut: anytime outlier detection on streaming data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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Sensor networks play an important role in applications concerned with environmental monitoring, disaster management, and policy making. Effective and flexible techniques are needed to explore unusual environmental phenomena in sensor readings that are continuously streamed to applications. In this paper, we propose a framework that allows to detect outlier sensors and to efficiently construct outlier regions from respective outlier sensors. For this, we utilize the concept of degree-based outliers. Compared to the traditional binary outlier models (outlier versus non-outlier), this concept allows for a more fine-grained, context sensitive analysis of anomalous sensor readings and in particular the construction of heterogeneous outlier regions. The latter suitably reflect the heterogeneity among outlier sensors and sensor readings that determine the spatial extent of outlier regions. Such regions furthermore allow for useful data exploration tasks. We demonstrate the effectiveness and utility of our approach using real world and synthetic sensor data streams.