big data and data mining :: Something smarter than us is going to emerge


node:	big data and data mining
template:	2
parent:	machine learning - practical. Marketing, content monetization
owner:	ulkas
viewed by:
created:	07.03.2016 - 08:57:24

cwbe coordinatez:
101
63533
8106902
8106903

ABSOLUT
KYBERIA

permissions
you:	r,
system:	public
net:	yes

sioux	0
sunrise	2

tbd

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https://clickhouse.yandex/
ClickHouse is an open-source column-oriented DBMS (columnar database management system) for online analytical processing (OLAP).

ClickHouse is an open source column-oriented database management system capable of real time generation of analytical data reports using SQL queries.

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Tuktu is a big data analytics platform that focuses on ease of use. The idea of the platform is that its users can focus on (business) logic rather than dealing with technical implementation details. This means that programming tedious pieces of code is a thing of the past. Tuktu comes with a couple of key characteristics:

Support for real-time and batch processing
Synchronous and asynchronous processing
Simple REST API
A visual modeller that allows to create jobs using drag-and-drop modelling
Its own distributed file system, called TDFS, that is very easy to use - alternatively, Tuktu integrates seamlessly with HDFS (and local files of course)
Its own in-memory distributed key-value store, called TuktuDB, for quickly storing and retrieving data
Built-in real-time visualization capabilities for a number of pre-defined visuals
Native support for web-analytics
Periodic (cron) jobs can be scheduled natively from Tuktu
By default there is no master/slave architecture, so no single point of failure
Switch seamlessly between distributed and local (even transactional) computation paradigms
Modular setup, with a number of modules like machine learning or social
Automatic generation of documentation using the meta-files in the modeller
Easy usage and setup: download the zip file and run the startup script (Windows, Mac and Linux supported)

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http://www-935.ibm.com/services/multimedia/using_a_predictive_analytics_model.pdf

This tutorial describes how data scientists and developers can build an application to predict flight delays. The app uses machine learning (Spark MLlib), fueled by publicly available airplane flight data and enriched with weather data, to predict flight delays due to weather conditions. In the event that a delay is likely, the app can also help determine the degree to which the flight will be delayed. To build the app, you will learn how to use a Get-Build-Analyze methodology and the IBM® Analytics for Apache® Spark™ service, which includes an interactive Jupyter Notebook. Learn how to: Collect data from various sources, including weather and flight patterns. Build the data collected, and test a predictive model. Analyze the data for business advantage, and quantify the impact of weather on flight delays. Get started building this flight prediction application, and gain inspiration for your next data science projects.

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Adobe sponsors a competition on vandalism detection, the WSDM Cup Challenge, awarding $2500 for the best-performing solutions that will also be published open source.

German scientists published the first machine learning-based approach on vandalism detection in Wikidata,

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http://www.ft.com/cms/s/2/50bb4830-6a4c-11e6-ae5b-a7cc5dd5a28c.html?siteedition=intl

"Now, a fresh shift is taking place. Just as divine authority was legitimised by religious mythologies, and human authority was legitimised by humanist ideologies, so high-tech gurus and Silicon Valley prophets are creating a new universal narrative that legitimises the authority of algorithms and Big Data. This novel creed may be called “Dataism”. "

uvedenie do perspektivy big data boom poslednych rokov a trochu desivy nahlad buducnosti

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dnes akcia free book:
https://www.packtpub.com/packt/offers/free-learning

Use R's powerful graphing capabilities to design and create professional-level graphics
Learn how to use Base R to analyze your data and generate statistical graphs
Create attractive graphics using advanced functions such as qplot and ggplot for research and analysis
A step-by-step guide, packed with examples using real-world data sets that can prove helpful to R programmers

stiahnut sa to da tu:
https://kyberia.sk/id/8190026

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vcera bla moznost free booku
https://www.packtpub.com/business/making-big-data-work-your-business

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nasa hackathlon:
https://2015.spaceappschallenge.org/challenge/

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Apache Hive 2.0 is Released
http://blog.cloudera.com/blog/2016/03/apache-hive-2-0-is-released/