You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Given @math distributed data streams @math , we consider the problem of estimating the number of unique identifiers in streams defined by set expressions over @math . We identify a broad class of algorithms for solving this problem, and show that the estimators output by any algorithm in this class are perfectly unbiased and satisfy strong variance bounds. Our analysis unifies and generalizes a variety of earlier results in the literature. To demonstrate its generality, we describe several novel sampling algorithms in our class, and show that they achieve a novel tradeoff between accuracy, space usage, update speed, and applicability.

Cited Abstract(s):
[1]: On a stream of two dimensional data items @math where @math is an item identifier, and @math is a numerical attribute, a correlated aggregate query requires us to first apply a selection predicate along the second ( @math ) dimension, followed by an aggregation along the first ( @math ) dimension. For selection predicates of the form @math , where parameter @math is provided at query time, we present new streaming algorithms and lower bounds for estimating statistics of the resulting sub stream of elements that satisfy the predicate. We provide the first sub linear space algorithms for a large family of statistics in this model, including frequency moments. We experimentally validate our algorithms, showing that their memory requirements are significantly smaller than existing linear storage schemes for large datasets, while simultaneously achieving fast per-record processing time. We also study the problem when the items have weights. Allowing negative weights allows for analyzing values which occur in the symmetric difference of two datasets. We give a strong space lower bound which holds even if the algorithm is allowed up to a logarithmic number of passes over the data(before the query is presented). We complement this with a small space algorithm which uses a logarithmic number of passes.
Tirthapura and Woodruff [1] give sketching algorithms for estimating @math queries for a special class of properties @math . Specifically, they consider streams that contain tuples of the form @math , where @math is a numerical parameter, and the subpopulation @math is specified via a lower or upper bound on @math .