The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Design and evaluation of a wide-area event notification service
ACM Transactions on Computer Systems (TOCS)
A scalable content-addressable network
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Distributed processing of very large datasets with DataCutter
Parallel Computing - Clusters and computational grids for scientific computing
A New Tree Type Data Structure with Homogeneous Nodes Suitable for a Very Large Spatial Database
Proceedings of the Sixth International Conference on Data Engineering
The many faces of publish/subscribe
ACM Computing Surveys (CSUR)
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
SplitStream: high-bandwidth multicast in cooperative environments
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Parallel Computing - Special issue: High performance computing with geographical data
Network-Aware Operator Placement for Stream-Processing Systems
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
IrisNet: An Architecture for a Worldwide Sensor Web
IEEE Pervasive Computing
Dynamic publish/subscribe to meet subscriber-defined delay and bandwidth constraints
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
Multilevel Predictions for the Aggregation of Data in Global Sensor Networks
DS-RT '10 Proceedings of the 2010 IEEE/ACM 14th International Symposium on Distributed Simulation and Real Time Applications
Scribe: a large-scale and decentralized application-level multicast infrastructure
IEEE Journal on Selected Areas in Communications
Moving range queries in distributed complex event processing
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
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Stream processing has evolved as a paradigm for efficiently sharing and integrating a massive amount of data into applications. However, the integration of globally dispersed sensor data imposes challenges in the effective utilization of the IT infrastructure that forms the global sensor network. Especially simulations require the integration of sensor streams at widely differing spatial and temporal resolutions. For current stream processing solutions it is necessary to generate a separate data stream for each requested resolution. Therefore, these systems suffer from high redundancy in data streams, wasting a significant amount of bandwidth and limiting their scalability. This paper presents a new approach to scalable distributed stream processing of data which stems from globally dispersed sensor networks. The approach supports applications in establishing continuous queries for sensor data at different resolutions and ensures efficient bandwidth usage of the data distribution network. Unlike existing work in the context of video stream processing which provides multiple resolutions by establishing separate channels for each resolution, this paper presents a stream processing system that can efficiently split/combine data streams in order to decrease/increase their resolution without loss in data precision. In addition the system provides mechanisms for load balancing of sensor data streams that allow efficient utilization of the bandwidth of the global sensor network.