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SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
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Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Data streams: algorithms and applications
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
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ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
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IEEE Transactions on Knowledge and Data Engineering
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ICDE '02 Proceedings of the 18th International Conference on Data Engineering
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CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
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ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
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Approximate counts and quantiles over sliding windows
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UberFlow: a GPU-based particle engine
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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DaMoN '05 Proceedings of the 1st international workshop on Data management on new hardware
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PDCN'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: parallel and distributed computing and networks
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
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ESA '08 Proceedings of the 16th annual European symposium on Algorithms
CellJoin: a parallel stream join operator for the cell processor
The VLDB Journal — The International Journal on Very Large Data Bases
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ACM Transactions on Database Systems (TODS)
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We present algorithms for fast quantile and frequency estimation in large data streams using graphics processors (GPUs). We exploit the high computation power and memory bandwidth of graphics processors and present a new sorting algorithm that performs rasterization operations on the GPUs. We use sorting as the main computational component for histogram approximation and construction of ε-approximate quantile and frequency summaries. Our algorithms for numerical statistics computation on data streams are deterministic, applicable to fixed or variable-sized sliding windows and use a limited memory footprint. We use GPU as a co-processor and minimize the data transmission between the CPU and GPU by taking into account the low bus bandwidth. We implemented our algorithms on a PC with a NVIDIA GeForce FX 6800 Ultra GPU and a 3.4 GHz Pentium IV CPU and applied them to large data streams consisting of more than 100 million values. We also compared the performance of our GPU-based algorithms with optimized implementations of prior CPU-based algorithms. Overall, our results demonstrate that the graphics processors available on a commodity computer system are efficient stream-processor and useful co-processors for mining data streams.