KNIME general.

Declutter - four tips for an efficient, fast workflow

Mon, 09/02/2019 - 10:00 admin

Recently on social media we asked you for tips on tidying up and improving workflows. Our aim was to find out how you declutter to make your workflows not just superficially neater, but faster, more efficient, and smaller: ultimately an elegant masterpiece! Check out the original posts on LinkedIn and Twitter.

Declutter - Four Tips for an Efficient, Fast Workflow
Fig. 1 From confusion to clarity - decluttering your workflow

How to pick the best approach to data science

Mon, 07/22/2019 - 10:00 berthold

The data science dilemma: Automation, APIs, or custom data science?

As companies place an increasing premium on data science, there is some debate about which approach is best to adopt — and there is no straight up, one-size-fits-all answer. It really depends on your organization’s needs and what you hope to accomplish.

There are three main approaches that have been discussed over the past couple of years; it’s worth taking a look at the merits and limitations of each as well as the human element involved. After all, knowing the capabilities of your team and who you’re attempting to serve with data science influences heavily how to implement it.

The KNIME Hub - Share and Collaborate

Mon, 07/15/2019 - 10:00 paolotamag

Authors: Paolo Tamagnini & Christian Dietz

Where To Get Answers to Your Data Science Questions?

When I start a new data science project with KNIME Analytics Platform, there are always a few questions I need to ask myself before I even pull in a single node to my blank workbench.

  • “Can I train this kind of a model in KNIME?”
  • “Which KNIME nodes will I need for this task?”
  • “Has anyone else put together a use case like this with KNIME before?”
  • “Can I download any KNIME workflows as inspiration?” 

To answer all these questions, all I need to do is ask the KNIME Hub. The KNIME Hub has been available at hub.knime.com since March 2019 but many new features have now been added with the release of KNIME Analytics Platform 4.0.

Will They Blend: KNIME meets OrientDB

Mon, 07/08/2019 - 10:00 Redfield

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 IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Will They Blend: Experiments in Data & Tool Blending

Tue, 06/11/2019 - 09:00 Lukasa

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 IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

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.

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