Here we are, 10 years later! It has been an incredible journey, both challenging and rewarding at the same time. Starting from an embryo idea in 2006, to make data analytics available and affordable to every data scientist in the world, we have embarked on this adventure with undefined expectations about the future. As you can often judge a book from its incipit, those initial steps gave some early indications about what the KNIME platform and the KNIME company would bring.
Definition of Customer Segments
Customer segmentation has undoubtedly been one of the most implemented applications in data analytics since the birth of customer intelligence and CRM data.
The concept is simple. Group your customers together based on some criteria, such as revenue creation, loyalty, demographics, buying behavior, or any combination of these criteria, and more.
The group (or segment) can be defined in many ways, depending on the data scientist’s degree of expertise and domain knowledge.
The latest version of KNIME Server 4.3 brings some additions to its REST interface. In this article I will present some of them and how they can be used by client programs. Before we start I should mention that the Mason specification that we are using as the response format has changed slightly and we have adapted KNIME Server accordingly. You may want to have a look at the current version in case you have been using the Mason metadata.
Author: Frank Dullweber, Böhringer Ingelheim / April 2016
Gene editing technology CRISPR-Cas
Modern biological research deals with approaches for modifying genetic information (genotype), i.e. the genetic makeup of a cell - and therefore of an organism. It is this information that determines the characteristics of that cell. In other words the change of the genotype can lead to an observable change of a cell or whole organism (phenotype).
Author: Julien Grossmann, Political and Violent Risk Data Analyst, HIS Economic and Country Risk
I’ve been using KNIME Analytics Platform for a year and a half, and in this time, KNIME has become a vital part of my work. As a political and violent risks data scientist, I am often confronted with incomplete or badly structured data. But with KNIME, I can always find ways to efficiently clean up, organize and analyze my data, and this, despite a total lack of programming or coding knowledge.
So let’s get started.
The KNIME Streaming Executor is an extension that currently "hides" inside KNIME Labs. Not many of our users are aware it exists, so let's find out what it can (and can't) do and why you should be interested.
Note. In KNIME Analytics Platform 3.3 or higher, a number of standard examples are in the folder called Example Workflows in the workspace. You’ll find a topic detector for social media, a recommendation engine to be used in retail, some classic examples for customer intelligence (churn prediction, credit scoring, and customer segmentation), and a few additional basics examples including data blending, reporting, and a simple predictive model training.
Supposing you have already downloaded the KNIME® Analytics Platform, here are 7 steps to make your learning phase more practical, more application oriented, and ultimately faster.
- Read the Welcome Page & install Extensions
- Explore the “Example Workflow”
- Get familiar with the workbench of the KNIME Analytics Platform
- Download another example workflow from the EXAMPLES server
- Change the example workflow to run on your data
- Optionally change the analytics and execute
- Read use case associated with example workflow
And what Jenkins has to do with sliced bread
If you are a KNIME user you are probably familiar with the mechanism that lets you install additional extensions and update an existing installation with later versions. If you are a KNIME developer you have probably wondered what kind of magic is involved to make this possible: getting from hundreds of line of Java code to an online update that allows users to install and update extensions. In this week's blog post we will reveal some (if not all) of this magic.