coansys ceon.pl

Content Analysis System

Our goal is to create an open source framework for mining very large collections of scientific publications. Content Analysis System CoAnSys will handle tens of millions of bibliographic records on a modest Hadoop cluster. We employ state-of-the-art machine learning techniques for document deduplication, author name disambiguation, citation matching, keyword extraction, and document analysis similarity, classification. All of this is implemented in Apache Hadoop. Oozie . CoAnSys uses HBase. Will be .

OVERVIEW

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LINKS TO COANSYS.CEON.PL

Scalar - Scala Conference in Central Europe - 11th of April 2015, Warsaw, Poland.

Become a sponsor next year. To his own surprise, sometimes writes a blog. Passionate about computer graphics and games. Passionate programmer, constantly looking for new things to learn and explore. He codes all the time unless doing something else.

WHAT DOES COANSYS.CEON.PL LOOK LIKE?

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COANSYS.CEON.PL SERVER

We detected that the main page on coansys.ceon.pl took nine hundred and sixty-nine milliseconds to load. Our parsers could not observe a SSL certificate, so our parsers consider coansys.ceon.pl not secure.
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213.135.60.97

SERVER SOFTWARE

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SITE TITLE

Content Analysis System

DESCRIPTION

Our goal is to create an open source framework for mining very large collections of scientific publications. Content Analysis System CoAnSys will handle tens of millions of bibliographic records on a modest Hadoop cluster. We employ state-of-the-art machine learning techniques for document deduplication, author name disambiguation, citation matching, keyword extraction, and document analysis similarity, classification. All of this is implemented in Apache Hadoop. Oozie . CoAnSys uses HBase. Will be .

PARSED CONTENT

The domain coansys.ceon.pl states the following, "Our goal is to create an open source framework for mining very large collections of scientific publications." I observed that the website said " Content Analysis System CoAnSys will handle tens of millions of bibliographic records on a modest Hadoop cluster." They also stated " We employ state-of-the-art machine learning techniques for document deduplication, author name disambiguation, citation matching, keyword extraction, and document analysis similarity, classification. All of this is implemented in Apache Hadoop."

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