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On Query Algebras for Probabilistic Databases
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Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Secure and highly-available aggregation queries in large-scale sensor networks via set sampling
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
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Efficient error estimating coding: feasibility and applications
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Optimal Monte Carlo integration with fixed relative precision
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Anytime approximation in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
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A typical approach to estimate an unknown quantity $\mu$ is to design an experiment that produces a random variable Z, distributed in [0,1] with E[Z]=\mu$, run this experiment independently a number of times, and use the average of the outcomes as the estimate. In this paper, we consider the case when no a priori information about Z is known except that is distributed in [0,1]. We describe an approximation algorithm ${\cal A}{\cal A}$ which, given $\epsilon$ and $\delta$, when running independent experiments with respect to any Z, produces an estimate that is within a factor $1+\epsilon$ of $\mu$ with probability at least $1-\delta$. We prove that the expected number of experiments run by ${\cal A}{\cal A}$ (which depends on Z) is optimal to within a constant factor {for every} Z.