debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

MLnick

import spark.streaming.StreamingContext._
import spark.streaming.{Seconds, StreamingContext}
import spark.SparkContext._
import spark.storage.StorageLevel
import spark.streaming.examples.twitter.TwitterInputDStream
import com.twitter.algebird.HyperLogLog._
import com.twitter.algebird._

/**
 * Example of using HyperLogLog monoid from Twitter's Algebird together with Spark Streaming's

sixtenbe

#!/usr/bin/python2


# Copyright (C) 2016 Sixten Bergman
# License WTFPL
#
# This program is free software. It comes without any warranty, to the extent
# permitted by applicable law. 
# You can redistribute it and/or modify it under the terms of the Do What The
# Fuck You Want To Public License, Version 2, as published by Sam Hocevar. See