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.

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.

Authors: Maarit Widmann & Casiana Rimbu

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.

Authors: Jakob Schröter & Marc Bux (KNIME)

Have you ever wondered how far you could push the boundaries of workflow design? Did you ever want to give free rein to your imagination when creating views in KNIME Analytics Platform? Do you also fancy the occasional round of cards? If the answer to either of these questions is “yes”, then this blog post is for you. Today, we’re creating our own game of blackjack in KNIME. We do this by using the Tile View (JavaScript) and CSS Editor nodes as well as the revised visual layout editor introduced in the latest KNIME release.

Authors: Giuseppe Di Fatta (University of Reading, UK) and Stefan Helfrich (KNIME)

Are you an expert in KNIME Analytics Platform? There is now an official way to answer this question and share it with the world: You can test your KNIME proficiency with a new certification program developed by a collaboration between academia and industry.

Professional certifications are particularly useful in the employment process to help identify key skills relevant to the job profile sought by employers. They facilitate matching the demand for skills with the offer at an earlier stage and also promote the need for the right skills. They help prospective applicants to understand the requirements in the current job market to plan their training and development more effectively. Employers can also use certifications to engage current employees in Continuous Professional Development (CDP) relevant to critical needs. While Higher Education degrees are evidence of a solid knowledge of a subject area (e.g., BSc Computer Science, MSc Data Science), certification programs tend to focus on very specific expertise and skills on industry-relevant tools and processes. Certification programs can help to ensure the right competence level is clearly identified and communicated. Certification tests are used to assess skills and knowledge for this purpose.

Author: Rosaria Silipo

Collaborative filtering (CF)^[1] based on the alternating least squares (ALS) technique^[2] is another algorithm used to generate recommendations. It produces automatic predictions (filtering) about the interests of a user by collecting preferences from many other users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B’s opinion on a different issue than a randomly chosen person. This algorithm gained a lot of traction in the data science community after it was used by the team winner of the Netflix Prize.

Authors: Andisa Dewi and Tobias Koetter

The focus today is to show how to perform data exploration and visualization on a large dataset using KNIME Big Data Extensions and make the whole process interactive via the KNIME WebPortal. The data that we will use is the hugely popular NYC taxi dataset.

The idea of this workflow is to explore the taxi dataset step by step. We start with a general overview of the entire dataset and then, in the following step, we filter directly right on the interactive view, e.g select the specific years we want information on, or choose a particular taxi type, then zoom in on the particular subset of data that we are most interested in. The next step involves visualizing the selected subset subsequently. The last step shows visualizations of taxi trips of a certain taxi type in a specific certain NYC borough over during certain years. All the visualizations are accessible via the KNIME WebPortal and the computation is done on a Hadoop cluster using the KNIME Big Data Extension.

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?

     KNIME user: “You got your notebook in my workflow!”

     Jupyter Python user: “You got your workflow in my notebook!”

     Both: “Oooo, they work great together!” ¹

KNIME Analytics Platform has had good integration with Python for quite a while. Since we think it’s important, we continue to invest in making improvements. There are two particularly exciting new Python-related features in the recent 3.7 release of KNIME Analytics Platform:

1. You can now use the Python code found in Jupyter notebooks from the Python scripting nodes in KNIME
2. You can now execute KNIME workflows directly from within Python. If you are working within a Jupyter notebook you can also get a (static) view of the workflow in the notebook.

Since the release of KNIME Server 4.7 operations with KNIME Server have been simplified thanks to the new feature of being able to manage client preferences. This feature allows KNIME Server administrators to define profiles for KNIME Analytics Platform users and makes it easy to enable KNIME Analytics Platform to support a wide range of technologies and databases across all major operating systems.