Summarizing Distributed Data Streams for Storage in Data Warehouses
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Continuous Trend-Based Clustering in Data Streams
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Tight results for clustering and summarizing data streams
Proceedings of the 12th International Conference on Database Theory
CAMS: OLAPing Multidimensional Data Streams Efficiently
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Spatial clustering of structured objects
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Relational mining in spatial domains: accomplishments and challenges
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Space-time roll-up and drill-down into geo-trend stream cubes
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Online and offline trend cluster discovery in spatially distributed data streams
MSM'10/MUSE'10 Proceedings of the 2010 international conference on Analysis of social media and ubiquitous data
Trend cluster based interpolation everywhere in a sensor network
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Integrating trend clusters for spatio-temporal interpolation of missing sensor data
W2GIS'12 Proceedings of the 11th international conference on Web and Wireless Geographical Information Systems
Trend cluster based kriging interpolation in sensor data networks
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
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We consider distributed computing environments where georeferenced sensors feed a unique central server with numeric and unidimensional data streams. Knowledge discovery fromthese geographically distributed data streams poses several challenges including the requirement of data summarization in order to store the streamed data in a central server with a limited memory. We propose an enhanced segmentation algorithm in order to group data sources in the same spatial cluster if they stream data which evolve according to a close trajectory over the time. A trajectory is constructed by tracking only data points which represent a change of trend in the associated spatial cluster. Clusters of trajectories are discovered on-the-fly and stored in the database. Experiments prove effectiveness and accuracy of our approach.