Did you ever ask yourself, while using KNIME Analytics Platform, “What should I do next?” Or “How can I use this node?” Or “What on earth is this parameter for?!” No matter if you are new to KNIME or already an expert, I’m sure you have asked these questions sometimes and might still be wondering about them.
There are many ways already to find the answers.
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?
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?
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?
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?
A short description of data analytics project formalization
through some of the whitepapers developed over time here at KNIME AG
It is not hard nowadays to find talks from conferences and blog posts on the web claiming that data analytics, or data science as it is now called, can do wonders for your company. Sure! However, identification of the relevant problems and their formalization into available data vs. the desired output remain the biggest obstacles to a realistic implementation of any data-driven project.
According to the W3C Linked Data page, the Semantic Web refers to a technology stack to support the “Web of data”. Semantic Web technologies enable people to create data stores on the Web, build vocabularies, and write rules for handling data. Linked data are empowered by technologies such as RDF, SPARQL, OWL, and SKOS.
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.
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.
