July 2022
Intermediate to advanced
304 pages
9h 15m
English
book
Algorithms and Data Structures for Massive Datasets
by Dzejla Medjedovic, Emin Tahirovic, Ines Schweigert
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8 Approximate quantiles on data streams
This chapter covers
- Reviewing the concept of exact quantiles and understanding constraints imposed by streaming data context
- Understanding different types of errors for approximate quantiles
- Applying t-digest and q-digest algorithms to a data stream
- Comparing t-digest and q-digest on realistic data on length of visits to a website
Different algorithms presented in the previous chapter allow us to select an (un)biased sample from all data-tuples that have arrived up to the current moment. In a way, a sample is a very flexible datasketch: you form it once, and you can then use it to claim that its mean, or any other feature, is a good estimate of that same feature of all the data from the stream so far. ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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