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(defn words [text] (re-seq #"[a-z]+" (.toLowerCase text)))
 
(defn train [features]
  (reduce (fn [model f] (assoc model f (inc (get model f 1)))) {} features))
 
(def *nwords* (train (words (slurp "big.txt"))))
 
(defn edits1 [word]
  (let [alphabet "abcdefghijklmnopqrstuvwxyz", n (count word)]
    (distinct (concat

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#!/usr/bin/env python
"""
Shows git branches sorted by last commit date, noting when branch has been
merged:

    $ git blast
    * master 33 minutes ago
      david 4 days ago [M]
      unholy-david-payments 4 days ago
      handsontable-2 5 days ago

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Direct links to free Springer books (pdf versions)

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

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;; Based on https://clojuredocs.org/clojure.core/condp#example-542692cbc026201cdc326bea

(defn parse-arg [s]
  (condp (comp next re-matches) s
    #"([+-])(\w+)" :>> (fn [[f k]] [(keyword k) (= f "+")])
    #"(\w+)=(\w+)" :>> (fn [[k v]] [(keyword k) v])
    (throw (IllegalArgumentException. s))))
  
(parse-arg "+foo")    ;=> [:foo true]
(parse-arg "-foo")    ;=> [:foo false]

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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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Business Models

Advertising

Models	Examples
Display ads	Yahoo!
Search ads	Google

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Latency Comparison Numbers
--------------------------
L1 cache reference                            0.5 ns
Branch mispredict                             5   ns
L2 cache reference                            7   ns             14x L1 cache
Mutex lock/unlock                            25   ns
Main memory reference                       100   ns             20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy              3,000   ns
Send 1K bytes over 1 Gbps network        10,000   ns    0.01 ms
Read 4K randomly from SSD*              150,000   ns    0.15 ms

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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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tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

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A friend asked me for a few pointers to interesting, mostly recent papers on data warehousing and "big data" database systems, and I figured I'd share the list. This is biased and rather incomplete but maybe of interest to someone. While many are obvious choices, I think there are a few underappreciated gems.

Dataflow/Stream Processing Engines:

Dryad--general-purpose distributed parallel dataflow engine
http://research.microsoft.com/en-us/projects/dryad/eurosys07.pdf

Google Dremel--columnar storage for fast queries (c.f. Impala)
http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/pubs/archive/36632.pdf