Coordinated Weighted Sampling for Estimating Aggregates Over Multiple Weight Assignments

TL;DR

This paper introduces a new coordinated weighted sampling framework for estimating aggregates over multiple weight assignments, significantly improving accuracy over previous methods, and demonstrates its effectiveness through extensive empirical evaluation.

Contribution

The paper develops a novel sampling framework for multiple weight assignments, providing estimators that are much tighter than existing approaches.

Findings

Estimators are orders of magnitude more accurate.

Framework is effective across diverse datasets.

Significantly improves aggregate estimation accuracy.

Abstract

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 $L_1$ 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 {\em scalar} weight is associated with each key. We develop a sampling framework based on {\em coordinated weighted samples} that is suited for multiple weight assignments and obtain estimators that are {\em 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.