KNIME: The Konstanz Information Miner
The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms
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1 Overview The need for modular data analysis environments has increased dramatically over the past years. In order to make use of the vast variety of data analysis methods around, it is essential that such an environment is easy and intuitive to use, allows for quick and interactive changes to the analysis process and enables the user to visually explore the results. To meet these challenges data pipelining environments have gathered incredible momentum over the past years. Some of today’s well-established (but unfortunately also commercial) data pipelining tools are InforSense KDE (InforSense Ltd.), Insightful Miner (Insightful Corporation), and Pipeline Pilot (SciTegic). These environments allow the user to visually assemble and adapt the analysis flow from standardized building blocks, which are then connected through pipes carrying data or models. An additional advantage of these systems is the intuitive, graphical way to document what has been done. KNIME, the Konstanz Information Miner provides such a pipelining environment. Figure 1 shows a screenshot of an example analysis flow. In the center, a flow is reading in data from two sources and processes it in several, parallel analysis flows, consisting of preprocessing, modeling, and visualization nodes. On the left a repository of nodes is shown. From this large variety of nodes, one can select data sources, data preprocessing steps, model building algorithms, as well as visualization tools and drag them onto the workbench, where they can be connected to other nodes. The
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Fig. 1. An example analysis flow inside KNIME.
ability to have all views interact graphically (visual brushing) creates a powerful environment to visually explore the data sets at hand. KNIME is written in Java and its graphical workflow editor is implemented as an Eclipse (Eclipse Foundation (2007)) plug-in. It is easy to extend through an open API and a data abstraction framework, which allows for new nodes to be quickly added in a well-defined way. In this paper we describe some of the internals of KNIME in more detail. More information as well as downloads can be found at http://www.knime.org.
2 Architecture The architecture of KNIME was designed with three main principles in mind. • •
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Visual, interactive framework: Data flows should be combined by simple drag&drop from a variety of processing units. Customized applications can be modeled through individual data pipelines. Modularity: Processing units and data containers should not depend on each other in order to enable easy distribution of computation and allow for independent development of different algorithms. Data types are encapsulated, that is, no types are predefined, new types can easily be added bringing along type specific renderers and comparators. New types can be declared compatible to existing types. Easy expandability: It should be easy to add new processing nodes or views and distribute them through a simple plugin mechanism without the need for complicated install/deinstall procedures.
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