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

sunaot

例外設計の話。

こんな指針がいいのかなー 2013 夏 ver.

例外の目的とは？

• 「例外をキャッチする主な目的は、エラーの原因を取り除いて、回復すること」
  • via http://dobon.net/vb/dotnet/beginner/exceptionhandling.html

.NET の「例外のデザインのガイドライン」にもこう書いてある。

repeatedly

MongoDB Casual Sucks

BSON!

皆さん実装したことあると思うので，言わなくても分かりますよね

https://github.com/repeatedly/Mongo-D-Driver

mmapper

rirakkumya

def index(id:String) = Action {
  getFirstData(id)
}
private def getFirstData(id:String) = {
  Cache.get(id) match {
    case Some(id2) => getSecondData(id2)
    case None => NotFound
  }
}
private def getSecondData(id2:String) = {

mala

#!perl

# perl jquery_patch.pl jquery-n.nn.min.js > jquery-n.nn.min.patched.js

$old = join "|", map quotemeta, (
 q{/^[^<]*(<(.|\s)+>)[^>]*$|^#(\w+)$/},
 q{/^[^<]*(<[\w\W]+>)[^>]*$|^#([\w-]+)$/},
 q{/^(?:[^<]*(<[\w\W]+>)[^>]*$|#([\w\-]+)$)/}
);
$new = q{/^(?:\s*(<[\w\W]+>)[^>]*$|#([\w\-]*)$)/};