visenger

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

visenger

import scala.util.parsing.json._

val result = JSON.parseFull("""
  {"name": "Naoki",  "lang": ["Java", "Scala"]}
""")

result match {
  case Some(e) => println(e) // => Map(name -> Naoki, lang -> List(Java, Scala))
  case None => println("Failed.")
}

visenger

# encoding: utf-8

'''
Script to convert the weltmodell into the conceptnet.

@author: Michael A. Huelfenhaus
'''
import sys
import csv
import hashlib

visenger

select numerator.x, numerator.num/denominator.denom, numerator.y, numerator.z, numerator.t
(select sum(x) as num, x, y, z, t from table group by x, y, z, t) numerator
join
(select sum(x) as denom, y, z, t from table group by y,z,t) denominator
on (numerator.y=denominator.y and numerator.z=denominator.z and numerator.t=denominator.t)