Random sampling with a reservoir
ACM Transactions on Mathematical Software (TOMS)
Size-estimation framework with applications to transitive closure and reachability
Journal of Computer and System Sciences
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Summary cache: a scalable wide-area web cache sharing protocol
IEEE/ACM Transactions on Networking (TON)
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Estimating simple functions on the union of data streams
Proceedings of the thirteenth annual ACM symposium on Parallel algorithms and architectures
Collection statistics for fast duplicate document detection
ACM Transactions on Information Systems (TOIS)
Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Mining database structure; or, how to build a data quality browser
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Distinct Sampling for Highly-Accurate Answers to Distinct Values Queries and Event Reports
Proceedings of the 27th International Conference on Very Large Data Bases
An Approximate L1-Difference Algorithm for Massive Data Streams
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
On the Resemblance and Containment of Documents
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Estimating flow distributions from sampled flow statistics
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Winnowing: local algorithms for document fingerprinting
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Sketch-based change detection: methods, evaluation, and applications
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Constructing a text corpus for inexact duplicate detection
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Improved robustness of signature-based near-replica detection via lexicon randomization
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Reversible sketches for efficient and accurate change detection over network data streams
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
A data streaming algorithm for estimating subpopulation flow size distribution
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS 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
What's new: finding significant differences in network data streams
IEEE/ACM Transactions on Networking (TON)
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
Fast range-summable random variables for efficient aggregate estimation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Spatially-decaying aggregation over a network
Journal of Computer and System Sciences
Detecting near-duplicates for web crawling
Proceedings of the 16th international conference on World Wide Web
On synopses for distinct-value estimation under multiset operations
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Finding similar files in a large file system
WTEC'94 Proceedings of the USENIX Winter 1994 Technical Conference on USENIX Winter 1994 Technical Conference
Summarizing data using bottom-k sketches
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Priority sampling for estimation of arbitrary subset sums
Journal of the ACM (JACM)
Why simple hash functions work: exploiting the entropy in a data stream
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Enriching network security analysis with time travel
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Hashed samples: selectivity estimators for set similarity selection queries
Proceedings of the VLDB Endowment
Tighter estimation using bottom k sketches
Proceedings of the VLDB Endowment
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
On the variance of subset sum estimation
ESA'07 Proceedings of the 15th annual European conference on Algorithms
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
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Many data sources are naturally modeled by multiple weight assignments over a set of keys: snapshots of an evolving database at multiple points in time, measurements collected over multiple time periods, requests for resources served at multiple locations, and records with multiple numeric attributes. Over such vector-weighted data we are interested in aggregates with respect to one set of weights, such as weighted sums, and aggregates over multiple sets of weights such as the L1 difference. Sample-based summarization is highly effective for data sets that are too large to be stored or manipulated. The summary facilitates approximate processing queries that may be specified after the summary was generated. Current designs, however, are geared for data sets where a single scalar weight is associated with each key. We develop a sampling framework based on coordinated weighted samples that is suited for multiple weight assignments and obtain estimators that are orders of magnitude tighter than previously possible. We demonstrate the power of our methods through an extensive empirical evaluation on diverse data sets ranging from IP network to stock quotes data.