ocramz

layout	title	date	comments	author	categories	commentIssueId
post	A comonad of graph decompositions	2016-04-02 13:02:54 -0400	true	Rúnar	scala comonads	22

ocramz

#!/usr/bin/env stack
-- stack --resolver=lts-6.0 runghc --package constraints --package mtl --package lens --package text --package time
{-# LANGUAGE KindSignatures #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE GADTs #-}
{-# LANGUAGE DataKinds #-}
{-# LANGUAGE FunctionalDependencies #-}
{-# LANGUAGE MultiParamTypeClasses, FlexibleInstances, ConstraintKinds, UndecidableInstances, ScopedTypeVariables, InstanceSigs, OverloadedStrings, TemplateHaskell #-}

-- This is short gist about problem I run today into,

ocramz

1. General Background and Overview

• 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

ocramz

{-# LANGUAGE DeriveFunctor, TypeFamilies #-}
module Success
  ( Parser(..)
  , parse
  ) where

import Control.Applicative
import Control.Monad
import Data.Bifunctor
import Data.Profunctor