Stephan Paukner :: syslog

• David MacKay: Information Theory, Pattern Recognition and Neural Networks: The Book

  tags: machinelearning books

  • Information Theory, Inference, and Learning Algorithms
  • 640 pages, Published September 2003
  • PDF (A4) pdf (9M) (fourth printing, March 2005)

• Cookbook for R » Cookbook for R

  HTML

  tags: r statistics programming

  • formerly named R Cookbook
  • It is not related to Paul Teetor’s excellent R Cookbook

• Big Data -- Why the 3Vs Just Don’t Make Sense -- TDWI -The Data Warehousing Institute

  When are we done defining big data? p.1

  tags: datascience bigdata opinion

  • settled on 3 Vs -- volume, variety, and velocity
  • another V: value
  • if big data is understood solely on the basis of these trends, it isn’t clear that it’s at all hype-worthy
  • if “big data” simply describes the volume, variety, and velocity of the information that constitutes it, our existing data management practices are still arguably up to the task
  • big data is hyped on the basis of its real or imagined outputs
  • a lot more interesting when you bring in ‘V’ for value

• No, Really, Some of My Best Friends Are Data Scientists

  When are we done defining data science?

  tags: datascience statistics opinion

  • the skills of a “data scientist” are those of a modern statistician
  • know how to move data around and manipulate data with some programming language
  • know how to draw informative pictures of data
  • Knowledge of stats, errorbars, confidence intervals
  • try to get people from different backgrounds
  • Great communication skills
  • a lot of what we teach The Kids now looks a lot more like machine learning than statistics as it was taught circa 1970, or even circa 1980
  • Everything I know about statistics I’ve learned without formal instruction
  • is not, in my experience, intrinsically hard for anyone who already has a decent grounding in some other mathematical science
  • mastering them really does mean trying to do things and failing
  • potentially hazardous. This is the idea that all that really matters is being “smart”
  • counter-productive for students to attribute their success or failure in learning about something to an innate talent

• Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (Wiley & SAS Business): Bill Franks

  Bill Franks is Chief Analytics Officer at Teradata

  tags: datascience bigdata books

  • 27. April 2012
  • 336 Seiten

• The Big Question In Big Data Is...What’s The Question? | SmartData Collective

  tags: datascience opinion bigdata

  • Some folks like to confuse Hadoop with big data
  • Focus On the Questions To Ask, Not The Answers
  • The failure of data warehouses to provide real-time data led to the creation of data marts
  • Data marts failed to provide complete and updated and comprehensive views
  • existing solutions still don’t solve the problem. Why? The market and business environment have changed
  • Data moves from structured to unstructured. Sources exponentially proliferate. Data quality is paramount.
  • Real-time is irrelevant because speed does not trump fidelity. Quantity does not trump quality
  • Business questions remained unanswered despite the massive number of reports and views and charts
  • The big shift is about moving from data to decisions

• Information Theory, Pattern Recognition, and Neural Networks

  tags: machinelearning video lectures

  • Draft videos (editing incomplete)
  • Entropy and Data Compression
  • Shannon’s Source Coding Theorem
  • Inference and Information Measures for Noisy Channels
  • Introduction to Bayesian Inference
  • Approximating Probability Distributions
  • Clustering
  • Monte Carlo Methods
  • Gibbs sampling
  • Neural Networks
  • Other course materials - free online text book [Information Theory, Inference, and Learning Algorithms]

Posted from Diigo. The rest of my favorite links are here.

It took me quite a long time to discover that my favorite knowledge management tool, Diigo, provides a feature to post one’s bookmarks to a blog. As I often had the desire to repost certain links I stumbled upon, I will do that occasionally from now on, mainly about everything from the topic pool of data mining (and related buzzwords), with flavors ranging from theory to applications, from technology to business. (I can’t really do that to social media sites, as it’s almost impossible to explicitly consume posts topic-wise. So, blogs aren’t really obsolete—yet.)

Btw, Diigo is really awesome: You can highlight text on webpages and add annotations to help understanding an article and create a summary on the fly, right while going through it. In this sense: If you want to be briefed, read at least this. (And don’t worry, the next episodes will contain less content; this one ranges back a few weeks.)

• Your Laptop Can Now Analyze Big Data - Technology Review

  tags: computerscience datascience technology software

  • GraphChi, exploits the capacious hard drives
  • a Mac Mini running GraphChi can analyze Twitter’s social graph from 2010—which contains 40 million users and 1.2 billion connections—in 59 minutes
  • The previous published result on this problem took 400 minutes using a cluster of about 1,000 computers
  • graph computation is becoming more and more relevant
  • GraphChi is capable of effectively handling many large-scale graph-computing problems without resorting to cloud-based solutions or supercomputers

• Large-scale Incremental Processing Using Distributed Transactions and Notifications

  Google’s Percolator paper

  tags: google datascience bigdata technology

  • Publication Year 2010
  • MapReduce and other batch-processing systems cannot process small updates  individually
  • Percolator, a system for incrementally processing updates to a large  data set

• Big Data in Deutschland – der Status Quo | silicon.de

  tags: bigdata datascience technology opinion

  • das Thema Big Data noch in einem frühen Stadium
  • noch in der Analyse- und Planungsphase
  • Verfügbarkeit neuer Analyse- und Datenbanktechnologien
  • dynamische Zunahme des unternehmensinternen Datenverkehrs
  • Big Data vielfach ‘durch die Hintertür’ ins Unternehmen
  • Datenwachstum von 42 Prozent bis zum Ende des Jahres 2014
  • auf Seiten der Storage-Infrastruktur eine Menge Arbeit
  • mittelständischen (500-999 Mitarbeiter) und
     den Großunternehmen (ab 1.000 Mitarbeiter)
  • Über ein Drittel erwarten sich Kosteneinsparungen. Fast die Hälfte verspricht sich bessere Einsichten in das Informations- und Konsumverhalten der Kunden
  • hohen Erwartungen, die an Dienstleister und Lösungsanbieter gestellt werden

• Why the days are numbered for Hadoop as we know it — Cloud Computing News

  tags: datascience bigdata cloud technology opinion

  • it has become synonymous with big data
  • de facto standard
  • Is the enterprise buying into a technology whose best day has already passed?
  • Hadoop’s inspiration – Google’s MapReduce
  • Google File System (GFS) and Google MapReduce (GMR)
  • make big data processing approachable to Google’s typical user/developer
  • Hadoop Distributed File System and Hadoop MapReduce — was born in the image of GFS and GMR
  • Your code is turned into map and reduce jobs, and Hadoop runs those jobs for you
  • Google evolved. Can Hadoop catch up?
  • GMR no longer holds such prominence in the Google stack
  • Here are technologies that I hope will ultimately seed the post-Hadoop era
  • it will require new, non-MapReduce-based architectures that leverage the Hadoop core (HDFS and Zookeeper) to truly compete with Google
  • Percolator for incremental indexing and analysis of frequently changing datasets
  • each time you want to analyze the data (say after adding, modifying or deleting data) you have to stream over the entire dataset
  • displacing GMR in favor of an incremental processing engine called Percolator
  • dealing only with new, modified, or deleted documents
  • Dremel for ad hoc analytics
  • SQL-like familiarity
  • many interface layers have been built
  • purpose-built for organized data processing (jobs). It is baked from the core for workflows, not ad hoc exploration
  • BI/analytics queries are fundamentally ad hoc, interactive, low-latency
  • Google invented Dremel (now exposed as the BigQuery product)
  • I’m not aware of any compelling open source alternatives to Dremel
  • Pregel for analyzing graph data
  • certain core assumptions of MapReduce are at fundamental odds with analyzing networks of people, telecommunications equipment, documents and other
  • petabyte -scale graph processing on distributed commodity machines
  • Hadoop, which often causes exponential data amplification in graph processing
  • execute graph algorithms such as SSSP or PageRank in dramatically shorter time
  • near linear scaling of execution time with graph size
  • the only viable option in the open source world is Giraph
  • if you’re trying to process dynamic data sets, ad-hoc analytics or graph data structures, Google’s own actions clearly demonstrate better alternatives to the MapReduce paradigm
  • Percolator, Dremel and Pregel make an impressive trio and comprise the new canon of big data
  • similar impact on IT as Google’s original big three of GFS, GMR, and BigTable