When we opened the door for December 6 on the Advent Calendar at KNIME HQ this year we discovered new releases of KNIME Analytics Platform and KNIME Server. Yeah! This happens every year, but it’s always exciting to release a bunch of cool new functionality for our community and customers to start using. We’ve added new views; better integration with Google Drive; even more new database nodes; integrations with Jupyter notebook, PySpark, and XGBoost; and many other things.

We’ve put together a couple of videos showing some of the highlights of this release:

Author: Rosaria Silipo.

There are many declinations of data science projects: with or without labeled data; stopping at data wrangling or involving machine learning algorithms; predicting classes or predicting numbers; with unevenly distributed classes, with binary classes, or even with no examples at all of one of the classes; with structured data and with unstructured data; using past samples or just remaining in the present; with requirements for real-time or close to real-time execution or with acceptably slower performances; showing the results in shiny reports or hiding the nitty gritty behind a neutral IT architecture; and with large budgets or no budget at all.

Authors: Chris Baddeley and Rosaria Silipo.

What is a Metanode?

Before we start, what is a metanode? Metanodes are gray nodes that contain sub-workflows. They play the role of functions or macros in script based tools.

They look like a single node, although they can contain many nodes and even more metanodes.

Recurrent Neural Networks (RNN) are the state of the art for sequence analysis ^{5 6}. With the release of KNIME Analytics Platform 3.6, KNIME extended its set of deep learning integrations, adding the Keras integration to the DL4J Integration. This adds considerably more flexibility and advanced layers, like RNN Layers.

In this article, we want to find out what Recurrent Neural Networks are in general, and LSTMs in particular. Let’s see where they are useful and how to set up and use the Keras integration in KNIME Analytics Platform to implement them.

As a use case for this particular application of RNNs and LSTMs we want to focus on automatic text generation. The question is: Can we teach KNIME to write a fairy tale?

Ever sat next to a friend or colleague at the computer and were awed when you suddenly realised the way they do certain tasks is much better? We recently asked KNIME users to share their tips and tricks on using KNIME. In this series of posts we’ll be showing you how the experts use KNIME in the hopes that by sharing ideas you’ll discover some handy techniques.

Viewing Content in the Workflow Editor

By Stephen Roughley

You can visualize two workflows (or metanode content) side by side inside the Workflow Editor of KNIME Analytics Platform.

To return the view to show only one editor, drag the additional editor tab to the tab panel in the main editor. Drop when you see a gray line appearing in the tab panel in the main editor.

Author: Paolo Tamagnini

The first step in data science is always data exploration, where we try to understand single attributes and their relationships with each other. Such exploratory analysis can be of two kinds: univariate and multivariate. We will limit the multivariate exploration here to bivariate exploration.

The univariate case considers data columns individually, while the bivariate case takes into account one pair of columns at a time.

Authors: Jeany Prinz & Oleg Yasnev

Today we will have a blast by travelling ∼270,000 years back in time. And we won’t even need a DeLorean, just our trusty KNIME Analytics Platform. We will return to the Ice Age and examine genetic material retrieved from a cave. We will solve the mystery as to which species the DNA came from.

To investigate this ancient DNA, we will utilize one of the most widely used bioinformatics applications: BLAST (Basic Local Alignment Search Tool). We will do that by running the BLAST RESTful interfaces to submit BLAST searches inside of KNIME Analytics Platform. This post speaks directly to people interested in bioinformatics. At the same time, because it covers the technical aspect of handling asynchronous REST operations, it will also be useful for those in other fields. Thus, if you have an interesting web application you want to use within KNIME - especially one where the resource method execution takes longer - keep reading!

Author: Srinivas Attili, Director – Marketing Analytics & Data Science at Juniper Networks

Often, organizations adopt tools and systems as per the emerging technology trends and their organic needs. This leads to a situation where a company’s technology landscape often resembles a forest of tools crowded in one place, however each tool is different from the next.

Information created by these diverse tools is often known only to a small group of people within the company and remains isolated from such pools created by various other systems. These unconnected islands of information need to be brought together, synthesized to derive insights that would not have been realized with the siloes of data.

Authors: Marten Pfannenschmidt and Paolo Tamagnini

There are two main analytics streams when it comes to social media: the topic and tone of the conversations and the network of connections. You can learn a lot about a user from their connection network!

Let’s take Twitter for example. The number of followers is often assumed to be an index of popularity. Furthermore, the number of retweets quantifies the popularity of a topic. The number of crossed retweets between two connections indicates the livelihood and strength of the connection. And there are many more such metrics.

@KNIME on Twitter counts more than 4500 followers (as of October 2018): the social niche of the KNIME real-life community. How many of them are expert KNIME users, how many are data scientists, how many are attentive followers of posted content?

Let’s check the top 20 active followers of @KNIME on Twitter and let’s arrange them on a chord diagram (Fig. 1).

Are you one of them?

Author: Peter Ohl

We are often asked two things about data privacy in KNIME:

• How are data handled in the open source KNIME Analytics Platform?
• Who has access to the data that are processed?

Before diving into the details, let’s first put this into context:

KNIME Analytics Platform is open source and has a huge community contributing to its functionality by developing many Community Extensions.