SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Size-estimation framework with applications to transitive closure and reachability
Journal of Computer and System Sciences
Approximate Query Processing: Taming the TeraBytes
Proceedings of the 27th International Conference on Very Large Data Bases
On the relationship between file sizes, transport protocols, and self-similar network traffic
ICNP '96 Proceedings of the 1996 International Conference on Network Protocols (ICNP '96)
Flow sampling under hard resource constraints
Proceedings of the joint international conference on Measurement and modeling of computer systems
Estimating arbitrary subset sums with few probes
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Sampling algorithms in a stream operator
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
The DLT priority sampling is essentially optimal
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Learn more, sample less: control of volume and variance in network measurement
IEEE Transactions on Information Theory
Priority sampling for estimation of arbitrary subset sums
Journal of the ACM (JACM)
Stream sampling for variance-optimal estimation of subset sums
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Leveraging discarded samples for tighter estimation of multiple-set aggregates
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Composable, scalable, and accurate weight summarization of unaggregated data sets
Proceedings of the VLDB Endowment
Coordinated weighted sampling for estimating aggregates over multiple weight assignments
Proceedings of the VLDB Endowment
Structure-aware sampling on data streams
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Structure-aware sampling on data streams
ACM SIGMETRICS Performance Evaluation Review - Performance evaluation review
Efficient Stream Sampling for Variance-Optimal Estimation of Subset Sums
SIAM Journal on Computing
Fair sampling across network flow measurements
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
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For high volume data streams and large data warehouses, sampling is used for efficient approximate answers to aggregate queries over selected subsets. We are dealing with a possibly heavy-tailed set of weighted items. We address the question: Which sampling scheme should we use to get the most accurate subset sum estimates? We present a simple theorem on the variance of subset sum estimation and use it to prove optimality and near-optimality of different known sampling schemes. The performance measure suggested in this paper is the average variance over all subsets of any given size. By optimal we mean there is no set of input weights for which any sampling scheme can have a better average variance. For example, we show that appropriately weighted systematic sampling is simultaneously optimal for all subset sizes. More standard schemes such as uniform sampling and probability-proportional-to-size sampling with replacement can be arbitrarily bad. Knowing the variance optimality of different sampling schemes can help deciding which sampling scheme to apply in a given context.