munk

(require 'mine-jabber)

;; Username & nickname fields from https://banno.hipchat.com/account/xmpp
(setq hipchat-username "")
(setq hipchat-nickname "")
(setq hipchat-password "")

(setq hipchat-autojoin-rooms
      '("aggregation" "assets" "banno_talk" "banno_cms" "data_services" "emacs" "grip" "kernel" "southen" "troll_talk" "webdev" "design_talk"))

munk

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

munk

# Python 3.4
 
import requests
import bs4 
 
def get_stock_price(name):
    url = "https://www.google.com/finance"
    data = requests.get(url, params={'q': name})
    bs = bs4.BeautifulSoup(data.text)
    div_price = bs.find(attrs={"id": "price-panel"})

munk

#!/bin/bash

set -eu
shopt -s nullglob

readonly base_dir=/var/lib/docker/registry
readonly output_dir=$(mktemp -d -t trace-images-XXXX)
readonly jq=/usr/bin/jq

readonly repository_dir=$base_dir/repositories