TonyAbell

cinst 1password
cinst 7zip
cinst 7zip.install
cinst AdobeAIR
cinst adobereader
cinst Atom
cinst markdownpad2
cinst autohotkey.portable
cinst beyondcompare
cinst curl

TonyAbell

#time "on"
#load "Bootstrap.fsx"

open System
open Akka.Actor
open Akka.Configuration
open Akka.FSharp
open Akka.TestKit

// #Using Actor

TonyAbell

There is a great purely functional streaming/IO library in Scala called scalaz-stream. It is itself based upon a Haskell library called machines. They provide powerful abstractions to express compositional effectful computations. These libraries rely on certain type system features in both Scala and Haskell which are unavailable in F# - namely existential types and higher kinds. Higher kinds allow the monad representing the side-effects to be abstracted over. If however we specialize this to a specific monad, we can get around the lack of existentials as well.

Tomas Petricek created a type called asynchronous sequence which provides similar capabilities. The F# AsyncSeq type is declared as follows:

type AsyncSeq<'a> = Async<AsyncSeqInner<'a>>

and AsyncSeqInner<'a> = 
    | Nil
 | Cons of 'a * AsyncSeq&lt;'a&gt;

TonyAbell

type CarsController() =
    inherit ApiController()

    let values = [| { Make = "Ford"; Model = "Mustang" }; { Make = "Nissan"; Model = "Titan" } |]

    /// Gets all values.
    member x.Get() = 
        let ok = x.Ok(values)
        let badRequest (msg:string) = x.BadRequest(msg)
        async { 

TonyAbell

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