Size-estimation framework with applications to transitive closure and reachability
Journal of Computer and System Sciences
New sampling-based summary statistics for improving approximate query answers
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mirror, mirror on the Web: a study of host pairs with replicated content
WWW '99 Proceedings of the eighth international conference on World Wide Web
A protocol-independent technique for eliminating redundant network traffic
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Mining database structure; or, how to build a data quality browser
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Maintaining time-decaying stream aggregates
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On the Resemblance and Containment of Documents
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Efficient estimation algorithms for neighborhood variance and other moments
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Spatially-decaying aggregation over a network: model and algorithms
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Estimating arbitrary subset sums with few probes
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
The DLT priority sampling is essentially optimal
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Weighted random sampling with a reservoir
Information Processing Letters
Confidence intervals for priority sampling
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Computing separable functions via gossip
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Spatially-decaying aggregation over a network
Journal of Computer and System Sciences
On synopses for distinct-value estimation under multiset operations
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Bottom-k sketches: better and more efficient estimation of aggregates
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Sketching unaggregated data streams for subpopulation-size queries
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Summarizing data using bottom-k sketches
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Algorithms and estimators for accurate summarization of internet traffic
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Priority sampling for estimation of arbitrary subset sums
Journal of the ACM (JACM)
Efficiently answering top-k typicality queries on large databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Learn more, sample less: control of volume and variance in network measurement
IEEE Transactions on Information Theory
Distinct-value synopses for multiset operations
Communications of the ACM - A View of Parallel Computing
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
Exponential time improvement for min-wise based algorithms
Information and Computation
A comparison of three algorithms for approximating the distance distribution in real-world graphs
TAPAS'11 Proceedings of the First international ICST conference on Theory and practice of algorithms in (computer) systems
Get the most out of your sample: optimal unbiased estimators using partial information
Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
gSketch: on query estimation in graph streams
Proceedings of the VLDB Endowment
Efficient Stream Sampling for Variance-Optimal Estimation of Subset Sums
SIAM Journal on Computing
Exponential time improvement for min-wise based algorithms
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Don't let the negatives bring you down: sampling from streams of signed updates
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
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Summaries of massive data sets support approximate query processing over the original data. A basic aggregate over a set of records is the weight of subpopulations specified as a predicate over records' attributes. Bottom-k sketches are a powerful summarization format of weighted items that includes priority sampling [22], and the classic weighted sampling without replacement. They can be computed efficiently for many representations of the data including distributed databases and data streams and support coordinated and all-distances sketches. We derive novel unbiased estimators and confidence bounds for subpopulation weight. Our rank conditioning (RC) estimator is applicable when the total weight of the sketched set cannot be computed by the summarization algorithm without a significant use of additional resources (such as for sketches of network neighborhoods) and the tighter subset conditioning (SC) estimator that is applicable when the total weight is available (sketches of data streams). Our estimators are derived using clever applications of the Horvitz-Thompson estimator (that is not directly applicable to bottom-k sketches). We develop efficient computational methods and conduct performance evaluation using a range of synthetic and real data sets. We demonstrate considerable benefits of the SC estimator on larger subpopulations (over all other estimators); of the RC estimator (over existing estimators for weighted sampling without replacement); and of our confidence bounds (over all previous approaches).