The DEDALE system for complex spatial queries
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The cougar approach to in-network query processing in sensor networks
ACM SIGMOD Record
The design of an acquisitional query processor for sensor networks
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
STREAM: the stanford stream data manager (demonstration description)
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
TelegraphCQ: continuous dataflow processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
SINA: scalable incremental processing of continuous queries in spatio-temporal databases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Snapshot Queries: Towards Data-Centric Sensor Networks
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
SECONDO: An Extensible DBMS Platform for Research Prototyping and Teaching
ICDE '05 Proceedings of the 21st 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
Distributed operation in the Borealis stream processing engine
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
MauveDB: supporting model-based user views in database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Moving Objects Databases (The Morgan Kaufmann Series in Data Management Systems) (The Morgan Kaufmann Series in Data Management Systems)
Supporting ranking and clustering as generalized order-by and group-by
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Continuous Nearest Neighbor Queries over Sliding Windows
IEEE Transactions on Knowledge and Data Engineering
Reconstructing domain boundaries within a given set of points, using Delaunay triangulation
Computers & Geosciences
Cluster By: a new sql extension for spatial data aggregation
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams
The VLDB Journal — The International Journal on Very Large Data Bases
New Data Types and Operations to Support Geo-streams
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
A data and query model for streaming geospatial image data
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
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This paper focuses on one important type of geo-streaming data - point geo-streams. Many interesting applications require selected discrete points with similar observations to be clustered according to spatial proximity and further elevated into higher-level spatial regions. Querying streaming point clusters as regions directly in a geo-stream database has many benefits, but is also very challenging. We propose two query optimization strategies, namely semantics-based optimization and incremental optimization for answering queries involving both point geo-streams and static data set. The experimental results on a streaming meteorological data set demonstrate the effectiveness and the efficiency of the query processing techniques. Compared with the baseline methods, our optimization methods can reduce the total execution time by more than an order of magnitude.