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asd

$$a_b+a^c$$

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package test;

import java.util.Random;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.ScheduledFuture;
import java.util.concurrent.TimeUnit;

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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

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#!/bin/bash
CNT=10
SLEEP=10
function show_help {
  echo "Usage: $0 -a auth_token -h host [-c count_per_iteration] [-s sleep_after_iteration]"
}

while getopts "h:c:s:a:?" opt; do
    case "$opt" in
    \?)

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    final boolean doReconnect() {
        Exception failure = null;
        synchronized (reconnectMutex) {

            // First ensure we are up to date.
            doUpdateURIsFromDisk();

            if (disposed || connectionFailure != null) {
                reconnectMutex.notifyAll();
            }