dbs-004.dvi
Foundations and Trends R � in Databases Vol. 4, Nos. 1–3 (2011) 1–294 c � 2012 G. Cormode, M. Garofalakis, P. J. Haas and C. Jermaine DOI: 10.1561/1900000004 Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches By Graham Cormode, Minos Garofalakis, Peter J. Haas and Chris Jermaine Contents 1 Introduction 2 1.1 The Need for Synopses 3 1.2 Survey Overview 5 1.3 Outline 6 2 Sampling 11
For Big Data, Moore’s Law Means Better Decisions | AMPLab – UC Berkeley
Data Drives Decisions Today, more and more organizations collect more and more data, and they do so with one goal in mind: extracting value. In most cases, this value comes in the form of decisions. There are myriad examples of data driving decisions: (1) monitoring network traffic to detect and defend against a cyber attack, (2) using clinical and genomic data to provide personalised medical trea
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A High-Performance Algorithm for Identifying Frequent Items in Data Streams
Counting uniques faster in BigQuery with HyperLogLog++ | Google Cloud Blog
GitHub - apache/datasketches-website: Website for DataSketches.
SIGCHI Conference Paper Format
http://data-people.cs.illinois.edu/courses/cs598/papers/incremental-fisher.pdf
GitHub - Wikinaut/agrep: AGREP - approximate GREP for fast fuzzy string searching. Files are searched for a string or regular expression, with approximate matching capabilities and user-definable records. Developed 1989-1991 by Udi Manber, Sun Wu et al. a
