We are getting close to the holiday season and, like every year, we have a new holiday version of KNIME ready to go under the Christmas tree!
KNIME 2.11 was released on December 6 featuring improvements in both the open source KNIME Analytics Platform and KNIME Big Data Extension.
Always with an eye to producing a tool for data-driven innovation, changes in this new version have faithfully followed the guidelines of the “Open for Innovation” manifesto.
Sentiment analysis of free-text documents is a common task in the field of text mining. In sentiment analysis predefined sentiment labels, such as "positive" or "negative" are assigned to texts. Texts (here called documents) can be reviews about products or movies, articles, etc.
In this blog post we show an example of assigning predefined sentiment labels to documents, using the KNIME Text Processing extension in combination with traditional KNIME learner and predictor nodes.
A set of 2000 documents has been sampled from the trainings set of the Large Movie Review Dataset v1.0. The Large Movie Review Dataset v1.0 contains 50000 English movie reviews along with their associated sentiment labels "positive" and "negative". For details about the data set see http://ai.stanford.edu/~amaas/data/sentiment/. We sampled 1000 documents of the positive group and 1000 documents of the negative group. The goal here is to assign the correct sentiment label to each document.
We will be welcoming participants to our 8th KNIME User Group Meeting and Workshops in the city of Berlin, Germany from February 23-27, 2015. The venue is the dbb forum in the heart of Berlin within walking distance of the Brandenburg Gate and the Reichstag building -- all only a few blocks away from the new KNIME offices.
In todays blog post I want to explore some different approaches to dealing with missing values in data sets in the KNIME Anlaytics Platform. Missing data is a problem that most people have to deal with at some point, and there are different approaches to doing so.
The KNIME Google API extension (since version 2.10) allows for the connection and interaction of KNIME with Google APIs. For now nodes are provided to request and load data from Google Analytics.
There has been a lot of talk about the Internet of Things lately, especially since the purchase of Nest by Google, officially opening the run towards intelligent household systems.
Intelligent means controllable from a remote location and capable of learning the inhabitants’ habits and preferences. Companies working in this field have multiplied over the last few years and some of them have been acquired by bigger companies, like SmartThings by Samsung for example.
Deduplication is the process of identifying redundant records in a data set referring to the same real-world entity and subsequently merging these together. Address data sets often contain slightly different records that represent identical addresses or names. Names of persons, streets, or cities may be written differently, are abbreviated, or misspelled. For example consider the following two addresses:
To deduplicate address data sets the records can be matched on a reference address data set in order to normalize their name and address notations.
With KNIME 2.10 new distance nodes have been released that allow the application of various distances measures in combination with the clustering nodes k-Medoids and Hierarchical Clustering, the Similarity Search node, and the Distance Matrix Pair Extractor node. Besides numerical distances such as p-norm distances (Euclidean, Manhattan, etc.), or cosine distance, also string distances, and byte and bit vector distances are provided. On top distances can be aggregated. If you still can't find the distance function you are looking for you can easily implement a customized distance with only one or two lines of Java code, using the Java Distance node. To get the nodes that make use of the new distances install the "KNIME Distance Matrix" extension (KNIME & Extensions -> KNIME Distance Matrix).
Familiar from common applications such as demographic analysis, visualizing election results, and mapping the outbreaks of disease, choropleths are an important visualization technique for aggregating spatial data. Today I want to discuss some techniques for generating such graphics in KNIME. So, to get us started, lets look at a very simple workflow to build such a visualisation.
If you are using the KNIME Server with the KNIME WebPortal in your organization you can easily share published workflows with your colleagues by using the URL parameters of the WebPortal. Not only is this a great way of linking directly to a specific workflow, but it also gives you the possibility to embed the WebPortal somewhere else in your corporate environment.
This powerful feature was introduced with KNIME Server 3.7 and some enhancements were added with version 3.8.
