big data and semantics

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http://wiki.dbpedia.org/UseCases

Use Cases

This page lists use cases for the DBpedia knowledge base together with references to ongoing work into these directions.

The Driving Force Behind Big Data: Data Connectivity

In most organizations, stakeholders maintain the perspective that Big Data offers tremendous benefits to the enterprise, especially when it comes to more agile business intelligence and analytics. Unfortunately, the days of complete visibility into Big Data are numbered – there is simply too much of it. While we may see companies promoting fancy strategies for managing ‘fire hose data’, only the ones focused on analytics will get close to creating meaning from the massive deluge. http://smartdatacollective.com/node/77641
http://googleresearch.blogspot.com/2009/06/large-scale-graph-computing-at-google.html Ed H. Chi, Staff Research Scientist

Large-scale graph computing at Google

AdaBoost

AdaBoost , short for Adaptive Boosting , is a machine learning algorithm, formulated by Yoav Freund and Robert Schapire . [ 1 ] It is a meta-algorithm , and can be used in conjunction with many other learning algorithms to improve their performance. AdaBoost is adaptive in the sense that subsequent classifiers built are tweaked in favor of those instances misclassified by previous classifiers. AdaBoost is sensitive to noisy data and outliers . http://en.wikipedia.org/wiki/AdaBoost
http://en.wikipedia.org/wiki/Quiver_(mathematics)

Quiver (mathematics)

In mathematics , a quiver is a directed graph where loops and multiple arrows between two vertices are allowed, i.e. a multidigraph . They are commonly used in representation theory : a representation , V , of a quiver assigns a vector space V ( x ) to each vertex x of the quiver and a linear map V ( a ) to each arrow a . In category theory , a quiver can be understood to be a category without identity morphisms and composition.

How to beat the CAP theorem - thoughts from the red planet - thoughts from the red planet

http://nathanmarz.com/blog/how-to-beat-the-cap-theorem.html The CAP theorem states a database cannot guarantee consistency, availability, and partition-tolerance at the same time. But you can't sacrifice partition-tolerance (see here and here ), so you must make a tradeoff between availability and consistency. Managing this tradeoff is a central focus of the NoSQL movement.
Amazon Elastic Compute Cloud (Amazon EC2) provides the flexibility to choose from a number of different instance types to meet your computing needs.

EC2 Instance Types

http://aws.amazon.com/ec2/instance-types/

Graph database

http://en.wikipedia.org/wiki/Graph_database A graph database uses graph structures with nodes, edges, and properties to represent and store data.
This web site is hosted in part by the Software and Systems Division , Information Technology Laboratory . This is a dictionary of algorithms, algorithmic techniques, data structures, archetypal problems, and related definitions. Algorithms include common functions, such as Ackermann's function .

Dictionary of Algorithms and Data Structures

http://xlinux.nist.gov/dads/
Seeking a Semantic Web Sweet Spot » AI3:::Adaptive Information .

Seeking a Semantic Web Sweet Spot

Working with Linked Data could be a little bit easier than it is, and a collaborative project between MediaEvent Services and the Freie Universität of Berlin aims to make it so.

Easing The Job Of Working With Linked Data

World's Leading Graph Database » What is Neo4j?

Learn about concepts behind Neo4j, graph databases, NOSQL and start to dive into our Cypher query language.