04 Mar 2016knime_admin

Thanks to Bob Muenchen (muenchen.bob@gmail.com) for permission to share his post from r4stats.com blog post of February 22, 2016
http://r4stats.com/2016/02/22/vcf/

That post refers to two graphs here labelled "Figure1: Gartner Magic Quadrant for 2016. What’s missing?" and "Figure2: Figure 2. Gartner Magic Quadrant for 2015". Since we do not have permission to show those graphs on our site, you might want to quickly click on the link to view them before returning here to read more.

The IT research firm, Gartner, Inc. has released its February 2016 report, Magic Quadrant for Advanced Analytics Platforms. The report’s main graph shows the completeness of each company’s vision plotted against its ability to achieve that vision (Figure 1.) I include this plot each year in my continuously updated article, The Popularity of Data Analysis Software, along with a brief summary of its major points.

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19 Feb 2016Iris

In surveys about the most-used tool for data analysis Excel always comes in as one of the most commonly used tools . It is taught in schools and used by countless companies. What you may not know, however, is that anything you can do with Excel you can also do using the nodes in KNIME Analytics Platform! This post is dedicated to getting you started if you already use Excel and want to migrate to KNIME Analytics Platform.

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05 Feb 2016jonfuller

The KNIME® Server REST API has already been covered from a design perspective in this blog post by Thorsten. My blog post aims to give you a hands on guide to getting started with the REST API. I’ll show some tools that you can use to get started and point you in the direction of some libraries that might help. But first I’ll discuss why you might want to use the REST API.

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25 Jan 2016knime_admin

The KNIME Spring Summit 2016 in Berlin is only a few weeks away. To give you a taste of what’s to come, today we are publishing the second in our series of interviews with the data scientists invited to present at the Spring Summit. We wanted to know just what drives them in their area of work or research and what their thoughts are on topics like data analytics, predictive analytics, the big data landscape and the internet of things.

Our interview today is with Stefan Weingaertner of Datatroniq GmbH, a KNIME Trusted Partner.

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08 Jan 2016kilian.thiel

Sentiment analysis of free-text documents is a common task in the field of text mining. In sentiment analysis predefined sentiment labels, such as "positive" or "negative" are assigned to text documents.

In a previous article we described how a predictive model was built to predict the sentiment labels of documents (positive or negative). In this approach single words were used as features. It was shown that the most discriminative terms w.r.t. separating the two classes are "bad", "wast", and "film". If the term "bad" occurs in a document, it is likely to have a negative sentiment. If "bad" does not occur but "wast" (stem of waste) it is again likely to score negatively, etc.

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06 Dec 2015heather.fyson

We at KNIME traditionally schedule releases twice a year – in summer and winter. The winter release is always on December 6. But why? In Germany, December 6 is St. Nicholas Day. On the night before, children put a shoe outside their bedroom doors. When they wake up in the morning they find their shoes filled with small gifts from St. Nicholas – aka Father Christmas. Legend has it that St. Nicholas enjoyed surprising people with presents. We like this particular tradition and it’s why we fill the KNIME community’s shoes with the latest version of our software on December 6.

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30 Nov 2015rs

The Newest Challenge

We are all witnessing the current data explosion: social media data, clinical data, system data, CRM data, web data, and lately tons of sensor data! With the advent of the Internet of Things, system and monitoring applications are producing humongous amounts of data, which undergo evaluation for a variety of reasons, for example to optimize costs and benefits, predict future events, classify behaviors, implement quality control, etc. All these use cases are relatively well established by now: a goal is defined, a target class is selected, a model is trained to recognize/predict the target, and the same model is applied to new never-seen-before productive data.

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19 Nov 2015knime_admin

Registration for the KNIME Spring Summit 2016 has just opened. To give you a taste of what’s to come, today we are publishing the first of a series of interviews with the speakers invited to present at the Spring Summit. We wanted to know just what drives them in their area of work or research and what their thoughts are on topics like data analytics, predictive analytics, the big data landscape and the internet of things.

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11 Nov 2015winter

2019-03-21: We added more comprehensive instructions that will be continually updated. Check out the new documentation here.

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