Nuno Edgar Nunes Fernandes nunofernandes-plight

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.
Models and Issues in Data Stream Systems
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
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep


	/*
	Basically "web3" comes from Mist,
	but "Web3" CAN come from the dapp.

	A Dapp has 3 ways to use web3.
	2. and 3. would work when in Mist and outside.
	*/

	// 1. simply use, web3 comes already defined

	// Open the wallet console: Menu -> Develop -> Toggle console ...
	// Run the following script

	_.each(Wallets.find().fetch(), function(item){
	if(item.address)
	Wallets.update(item._id, {$set: {address: item.address.toLowerCase()}});
	});
	_.each(CustomContracts.find().fetch(), function(item){
	if(item.address)
	CustomContracts.update(item._id, {$set: {address: item.address.toLowerCase()}});

	library(mnormt)
	mycols <- topo.colors(100,0.5)
	xhat <- c(0.2, -0.2)
	Sigma <- matrix(c(0.4, 0.3,
	0.3, 0.45), ncol=2)
	x1 <- seq(-2, 4,length=151)
	x2 <- seq(-4, 2,length=151)
	f <- function(x1,x2, mean=xhat, varcov=Sigma)
	dmnorm(cbind(x1,x2), mean,varcov)
	z <- outer(x1,x2, f)

	#The continuation of Python mid-level Course
	flying_through = 'continuation mode'