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

ion1

#N canvas 980 79 927 1121 10;
#X msg -1327 -1755 play;
#X obj -1197 -1786 route ready samplerate length cache float bang;
#X obj -1388 -1719 readanysf~ 2;
#X obj -972 -1755 bng 15 250 50 0 empty empty empty 17 7 0 10 -262144
-1 -1;
#X floatatom -1197 -1744 5 0 0 0 - - -;
#X floatatom -1151 -1755 5 0 0 0 - - -;
#X floatatom -1107 -1755 5 0 0 0 - - -;
#X floatatom -1061 -1755 5 0 0 0 - - -;

colinbull

type UnionTypeConverter() =
    inherit JsonConverter()

    let doRead pos (reader: JsonReader) = 
        reader.Read() |> ignore //pop start obj type label
       // printfn "%sRead %s %A %A" ("".PadLeft(reader.Depth)) pos reader.Value reader.TokenType 

    override x.CanConvert(typ:Type) =
        let result = 
            ((typ.GetInterface(typeof<System.Collections.IEnumerable>.FullName) = null)