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:
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of data matrices that arise from large-scale scientific simulations and data collection. The technical contribution consists in a new algorithm for constructing an accurate low-rank approximation of a matrix from streaming data. This method is accompanied by an a priori analysis that allows the user to set algorithm parameters with confidence and an a posteriori error estimator that allows the user to validate the quality of the reconstructed matrix. In comparison to previous techniques, the new method achieves smaller relative approximation errors and is less sensitive to parameter choices. As concrete applications, the paper outlines how the algorithm can be used to compress a Navier--Stokes simulation and a sea surface temperature dataset.

Cited Abstract(s):
[1]: The frequency moments of a sequence containing mi elements of type i, for 1 i n, are the numbers Fk = P n=1 m k . We consider the space complexity of randomized algorithms that approximate the numbers Fk, when the elements of the sequence are given one by one and cannot be stored. Surprisingly, it turns out that the numbers F0;F1 and F2 can be approximated in logarithmic space, whereas the approximation of Fk for k 6 requires n (1) space. Applications to data bases are mentioned as well.

[2]: This paper presents algorithms for tracking (approximate) join and self-join sizes in limited storage, in the presence of insertions and deletions to the data set(s). Such algorithms detect changes in join and self-join sizes without an expensive recomputation from the base data, and without the large space overhead required to maintain such sizes exactly. Query optimizers rely on fast, high-quality estimates of join sizes in order to select between various join plans, and estimates of self-join sizes are used to indicate the degree of skew in the data. For self-joins, we consider two approaches proposed in N. (The space complexity of approximating the frequency moments, J. Comput. System Sci.58 (1999), 137?147), which we denote tug-of-war and sample-count . We present fast algorithms for implementing these approaches, and extensions to handle deletions as well as insertions. We also report on the first experimental study of the two approaches, on a range of synthetic and real-world data sets. Our study shows that tug-of-war provides more accurate estimates for a given storage limit than sample-count, which in turn is far more accurate than a standard sampling-based approach. For example, tug-of-war needed only 4-256 memory words, depending on the data set, in order to estimate the self-join size to within a 15 relative error; on average, this is over 4 times (50 times) fewer memory words than needed by sample-count (standard sampling, resp.) to obtain a similar accuracy. For joins, we propose schemes based on maintaining a small signature of each relation independently, such that join sizes can be quickly and accurately estimated between any pair of relations using only these signatures. We show that taking random samples for join signatures can lead to an inaccurate estimation unless the sample size is quite large; moreover, we show that no other signature scheme can significantly improve upon sampling without further assumptions. These negative results are shown to hold even in the presence of sanity bounds. On the other hand, we present a fast join signature scheme based on tug-of-war signatures that provides guarantees on join size estimation as a function of the self-join sizes of the joining relations; this scheme can significantly improve upon the sampling scheme.

[3]: 1 Introduction 2 Map 3 The Data Stream Phenomenon 4 Data Streaming: Formal Aspects 5 Foundations: Basic Mathematical Ideas 6 Foundations: Basic Algorithmic Techniques 7 Foundations: Summary 8 Streaming Systems 9 New Directions 10 Historic Notes 11 Concluding Remarks Acknowledgements References.
Sketching algorithms are specifically designed for the streaming model; that is, for data that is presented as a sequence of updates. The sketching paradigm is attributed to [1] [2] ; see the survey [3] for an introduction and overview of early work.