KNIME ( ), Konstanz Information Miner , is an open source, free analytics, reporting and data integration platform. KNIME integrates components for machine learning and data mining through its modular data piping concept. The graphical user interface and the use of JDBC allow node assembly to integrate different data sources, including preprocessing (ETL: Extraction, Transformation, Loading), for modeling, data analysis and visualization without, or with minimal programming. To some extent as KNIME's advanced analytical tools can be regarded as an alternative to SAS.
Since 2006, KNIME has been used in pharmaceutical research, also used in other fields such as CRM customer data analysis, business intelligence, and financial data analysis.
Video KNIME
Histori
KNIME development began in January 2004 by a team of software engineers at Konstanz University as an exclusive product. The original developer team led by Michael Berthold comes from a company in Silicon Valley that provides software for the pharmaceutical industry. The initial goal is to create a highly scalable, open-ended, modular data processing platform that enables easy integration of multiple data loading, processing, transformation, analysis and visual exploration modules without focusing on specific application areas. The platform is intended to be a collaborative and research platform and also serves as an integration platform for many other data analysis projects.
In 2006 the first version of KNIME was released and several pharmaceutical companies started using KNIME and a number of life science software vendors began to integrate their tools into KNIME. Later that year, after an article in German magazine, users from a number of other areas joined the ship. As of 2012, KNIME is used by more than 15,000 actual users (i.e., excluding downloads, but users regularly take updates when available) not only in life sciences but also in banks, publishers, car manufacturers, telecommunications companies, consulting firms, other industries but also in a large number of research groups around the world. The latest update to KNIME Server and KNIME Big Data Extensions, provides support for Apache Spark 2.0.
At Gartner Quadrant 2018 KNIME Is among the top 4 products.
Maps KNIME
Internal
KNIME allows users to visually create data streams (or pipelines), selectively run some or all of the analysis steps, and then examine interactive results, models, and views. KNIME is written in Java and based on Eclipse and uses its extension mechanism to add plugins that provide additional functionality. The core version includes hundreds of modules for data integration (I/O files, database nodes support all common database management systems via JDBC or native connectors: SQLite, SQL Server, MySQL, PostgreSQL, Vertica and H2), data transformations (filters, converters, splitter, combiner, joiner) as well as commonly used statistical methods, data mining, text analysis and analysis. Visualization support with free Designer Report extension. The KNIME workflow can be used as a data set to create exportable report templates to document formats such as doc, ppt, xls, pdf and more. Other KNIME capabilities are:
- KNIME core architecture allows processing of large data volumes limited only by available hard disk space (not limited to available RAM). For example. KNIM allows analysis of 300 million customer addresses, 20 million cell images, and 10 million molecular structures.
- Additional plugins allow the integration of methods for Text Mining, Drawing Images, as well as time series analysis.
- KNIME integrates various other open source projects, e.g. the machine learning algorithm from Weka, the R statistics package project, as well as LIBSVM, JFreeChart, ImageJ, and the Chemical Development Kit.
KNIM is implemented in Java but also enables wrapping calling code other than providing nodes that allow to run Java, Python, Perl and other code fragments.
License
In version 2.1, KNIME was released under GPLv3 with the exception of allowing others to use a well defined API node to add exclusive extensions. It also allows commercial SW vendors to add wrappers that call their tools from KNIME.
See also
- Weka - machine learning algorithm that can be integrated in KNIME
- ELKI - data mining framework with many grouping algorithms
- NodePit - the world's first search engine for vertices and workflows that let you explore community variations.
References
External links
- KNIM Home
- KNIME Labs
- Eclipse Project
- Introductory text for KNIME
- KNIME advanced user manual
- KNIME and beginners
- KNIME for Cheminformatics
- KNIME SchrÃÆ'¶dinger extension
- Chemical Computing Group - MOE Extension for KNIME
- Selenium Node - Place your web browser to work with KNIME
- NodePit - Search engine for knot and KNIM workflow
Source of the article : Wikipedia
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