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

tonymorris

import annotation._

case class Pronunciation(pronounce: String) extends StaticAnnotation
case class Law(name: String, denotation: String) extends StaticAnnotation

trait ~>[F[_], G[_]] {
  def apply[A]: F[A] => G[A]
}

case class Id[A](x: A)

swlaschin

(*
This script analyzes the dependencies between top level types in a .NET Assembly.
It is then used to compare the dependency relationships in some F# projects with those in some C# projects.

Note that no attempt has been made to optimize the code yet! 

REQUIRES:

* Mono.Cecil for code analysis
  From http://www.mono-project.com/Cecil#Download 

tonymorris

Moved to https://github.com/tonymorris/type-class