rafaelgarrote

package org.kafecho.learning.akka

import java.net.InetSocketAddress
import akka.actor.Actor
import akka.actor.ActorLogging
import akka.actor.ActorSystem
import akka.actor.Props
import akka.actor.actorRef2Scala
import akka.io.IO
import akka.io.UdpFF

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

jcraane

<?xml version="1.0" encoding="UTF-8"?>
<configuration scan="true">
    <appender name="consoleAppender" class="ch.qos.logback.core.ConsoleAppender">
        <encoder>
            <charset>UTF-8</charset>
            <Pattern>%d %-4relative [%thread] %-5level %logger{35} - %msg%n</Pattern>
        </encoder>
    </appender>

    <appender name="FILE" class="ch.qos.logback.core.rolling.RollingFileAppender">