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

michaelfairley

-module(bowling).
-export([main/1]).

main(_) ->
    test().

test() ->
    % empty
    [] = score([]),
    % boring

anaisbetts

    public class FuncServiceLocator
    {
        Dictionary<Tuple<Type, string>, List<Func<object>>> _registry;

        public void Register(Func<object> factory, Type type, string contract = null)
        {
            var pair = Tuple.Create(type, contract ?? "");
            if (!_registry.ContainsKey(pair)) _registry[pair] = new List<Func<object>>();

            _registry[pair].Add(factory);

kevsmith

-module(bench).

-export([run_suite/3, run/1]).

run_suite(Name, Passes, Messages) ->
    Results = run_suite1(Passes, Messages, []),
    Avg = lists:sum(Results) / Passes,
    StdDev = math:sqrt(lists:sum([math:pow(X - Avg, 2) || X <- Results]) / Passes),
    Summary = [{avg, Avg}, {stddev, StdDev}, {min, lists:min(Results)},
               {max, lists:max(Results)}],