Highly scalable trip grouping for large-scale collective transportation systems
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Increasing availability of industrial systems through data stream mining
Computers and Industrial Engineering
Scalable splitting of massive data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
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Scientific instruments, such as radio telescopes, colliders, sensor networks, and simulators generate very high volumes of data streams that scientists analyze to detect and understand physical phenomena. The high data volume and the need for advanced computations on the streams require substantial hardware resources and scalable stream processing. We address these challenges by developing data stream management technology to support high-volume stream queries utilizing massively parallel computer hardware. We have developed a data stream management system prototype for state-of-the-art parallel hardware. The performance evaluation uses real measurement data from LOFAR, a radio telescope antenna array being developed in the Netherlands.