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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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## chef report handler for Consul
## depends on a chef-client check being registered with the agent
## • pings TTL-style check with ok/warning status on chef run pass/fail
## • stores run report and node attributes in chef/reports/<datacenter>/<consul_node_name>

require "chef/handler"
require "rest_client"

class ConsulHandler < Chef::Handler
    attr_reader :config

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print("hello world!")