Nile: A Query Processing Engine for Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
SPASS: scalable and energy-efficient data acquisition in sensor databases
Proceedings of the 4th ACM international workshop on Data engineering for wireless and mobile access
Nile-PDT: a phenomenon detection and tracking framework for data stream management systems
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Detection and tracking of discrete phenomena in sensor-network databases
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Scalability Management in Sensor-Network PhenomenaBases
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Dynamics-aware similarity of moving objects trajectories
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Sharing and exploring sensor streams over geocentric interfaces
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Tracking deformable 2D objects in wireless sensor networks
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Phenomenon-Aware Stream Query Processing
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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Geographically co-located sensors tend to participate in the same environmental phenomena. Phenomenon-aware stream query processing improves scalability by subscribing each query only to a subset of sensors that participate in the phenomena of interest to that query. In the case of sensors that generate readings with a multi-attribute schema, phenomena may develop across the values of one or more attributes. However tracking and detecting phenomena across all attributes does not scale well as the dimensions increase. As the size of sensor network increases, and as the number of attributes being tracked by a sensor increases this becomes a major bottleneck. In this paper, we present a novel n-dimensional Phenomenon Detection and Tracking mechanism (termed as nd-PDT) over n-ary sensor readings. We reduce the number of dimensions to be tracked by first dropping dimensions without any meaningful phenomena, and then we further reduce the dimensionality by continuously detecting and updating various forms of functional dependencies amongst the phenomenon dimensions.