The space complexity of approximating the frequency moments
Journal of Computer and System Sciences
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
On distributing symmetric streaming computations
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Lower bounds on frequency estimation of data streams
CSR'08 Proceedings of the 3rd international conference on Computer science: theory and applications
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Data stream computations in domains such as internet applications are often performed in a highly distributed fashion in order to save time. An example is the class of applications that use the Google Mapreduce framework of scalable distributed processing as presented by (Dean & Ghemawat 2004). A basic question here is: what kind of data stream computations admit scalable and efficient distributed algorithms? We show that the class of data stream computations that approximate functions of the frequency vector of the stream can be computed efficiently in a distributed manner.