Coordinated weighted sampling for estimating aggregates over multiple weight assignments

Edith Cohen*, Haim Kaplan, Subhabrata Sen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 vectorweighted 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.

Original languageEnglish
Pages (from-to)646-657
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number1
StatePublished - 2009


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