KNIME general.

Embedding KNIME in a Manufacturing Environment

Mon, 06/03/2019 - 10:00 admin

Author: Brendan Doherty, Seagate Technology

In this blog post I will discuss some of the processes and steps that were taken on the journey to embed KNIME in a High Volume Manufacturing Environment within Seagate Technology.

Seagate Technology are one of the world's largest manufacturers of electronic data storage technologies and solutions. Seagate Technology creates products and services that include network attached storage, high performance computing, data protection appliances, internal hard drives, backup and recovery services, flash storage, and related solutions. They are a vertically integrated company and have manufacturing plants based in many locations worldwide. The read/write heads for the HDDs are manufactured in two locations, one of which is in Derry City, Northern Ireland. These devices are highly complex, have a long manufacturing cycle time, and generate a lot of data during their fabrication. The plant has many different groups located at the site all of which use data from a wide variety of sources on a daily basis.

From A for Analytics to Z for Zika Virus

Mon, 05/20/2019 - 10:00 Jeany

Author: Jeany Prinz

A for Analytics Platform to Z for Zika Virus

One of the great advantages of KNIME Analytics Platform is its ability to analyze diverse types of data. Today we want to move to the outer edge of the alphabet and look into data from the Zika virus. Our analysis is inspired by the Microreact project1Zika virus in the Americas2 and is a nice use case for the exploration of epidemiological data with KNIME Analytics Platform. Epidemiology is the study of the distribution and determinants of health-related states or events3. In this post, therefore, we will answer the question: What routes did the Zika virus take as it spread across the globe, and how did its genetic makeup change the way it did? To this end, we will investigate and visualize both geolocational and phylogenetic data from the Zika virus. Using generic JavaScript nodes, we will create our own dynamic views and wrap them into a composite interactive view.

Even if you deal with very different data on a day-to-day basis, this blog post is still of high value, as we show how to increase the flexibility of your analysis using interactive generic JavaScript views.

Guided Automation for Machine Learning, Part II

Mon, 05/13/2019 - 10:00 admin

Authors: Paolo Tamagnini, Simon Schmid, and Christian Dietz

Implementing a Web-based Blueprint for Semi-automated Machine Learning, using KNIME Analytics Platform

This article is a follow-up to our introductory article on the topic, “How to automate machine learning.” In this second post, we describe in more detail the techniques and algorithms happening behind the scenes during the execution of the web browser application, proposing a blueprint solution for the automation of the machine learning lifecycle.

The price to pay for automated machine learning (aka AutoML) is the loss of control to a black box kind of model. While such a price might be acceptable for circumscribed data science problems on well-defined domains, it might prove a limitation for more complex problems on a wider variety of domains. In these cases, a certain amount of interaction with the end users is actually desirable. This softer approach to machine learning automation — the approach we take at KNIME — is obtained via guided automation, a special instance of guided analytics.

How to automate machine learning

Will They Blend? Today: Twitter and Azure. Sentiment Analysis via API.

Mon, 05/06/2019 - 10:00 craigcullum

Author: Craig Cullum

In this blog series we’ll be experimenting with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: will they blend? Want to find out what happens when website texts and Word documents are compared?

Follow us here and send us your ideas for the next data blending challenge you’d like to see at willtheyblend@knime.com.

Today: Twitter and Azure. Sentiment Analysis via API.

How to Automate Machine Learning

Mon, 04/08/2019 - 10:00 admin

Authors: Paolo Tamagnini, Simon Schmid, and Christian Dietz

Is it possible to fully automate the data science lifecycle? Is it possible to automatically build a machine learning model from a set of data?

Indeed, in recent months, many tools have appeared that claim to automate all or parts of the data science process. How do they work? Could you build one yourself? If you adopt one of these tools, how much work would be necessary to adapt it to your own problem and your own set of data?

Use Deep Learning to Write Like Shakespeare

Mon, 03/25/2019 - 10:00 admin

Author: Rosaria Silipo

LSTM recurrent neural networks can be trained to generate free text.
Let’s see how well AI can imitate the Bard.

“Many a true word hath been spoken in jest.”
― William Shakespeare, King Lear

“O, beware, my lord, of jealousy;
It is the green-ey’d monster, which doth mock
The meat it feeds on.”
― William Shakespeare, Othello

Text Encoding: A Review

Mon, 03/18/2019 - 10:00 admin

Authors: Rosaria Silipo and Kathrin Melcher

The key to perform any text mining operation, such as topic detection or sentiment analysis, is to transform words into numbers, sequences of words into sequences of numbers. Once we have numbers, we are back in the well-known game of data analytics, where machine learning algorithms can help us with classifying and clustering.

We will focus here exactly on that part of the analysis that transforms words into numbers and texts into number vectors: text encoding.

Market Simulation with KNIME: Android vs iOS

Mon, 03/11/2019 - 10:00 admin

Author: Ted Hartnell (CTO of Scientific Strategy)

What is Market Simulation?

A market simulation is a way to model a real world market. Just as real world markets have products, features, brands, stores, locations, and competitive rivals, so does a market simulation. But what makes a market simulation truly realistic are the customers. Simulations can generate tens of thousands of virtual customers designed to mimic the purchase decisions of real world shoppers. Customers evaluate the differentiation offered by each product.

How to Build Pivot Tables - A Vlog

Mon, 03/04/2019 - 10:00 admin

Authors: Maarit Widmann & Casiana Rimbu

How to build a pivot table in KNIME Analytics Platform

To pivot or not to pivot, that is the question.

Did you know that a pivot table allows you to quickly summarize your data based on a group, pivot, and aggregation columns? This summary might include sums, averages, or other statistics, which the pivot table splits the statistics is a meaningful way for different subgroups and draws attention to useful information.

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