Daniel Truemper truemped

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

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

Business Models

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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

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

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

	(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

	#!/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

	;; 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]

	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