DNP 825 Assignment The KNIME

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DNP 825 Assignment The KNIME

DNP 825 Assignment The KNIME

This assignment consist of two parts.

Part A

Access the Konstanz Information Miner tool at the KNIME web
site: http://www.knime.org/knime

Review the information about the KNIME Analytics Platform.

Answer the following questions:

How could this platform be used to analyze health care
data?

What benefits does this software have in comparison to commercial
products that have similar functionality and use?

Access the journal article by Raghupathi, W., Raghupathi,
V., (2014) Big data analytics in healthcare: promise and potential. Health
Information Science and Systems 2(3), 1-10 http://www.hissjournal.com/content/2/1/3

Read the article and define the 4 “Vs” of big data analytics
in health care and give an example of each.

Combine your findings writing a 5 page paper using APA
format. The title page and reference
page is not included in the page count.

What Is KNIME?

KNIME stands for Konstanz Information Miner. The KNIME platform is open source and designed for data analysis and reporting. It’s written in Java and built on Eclipse. There are extensions available with additional features.

The platform has machine learning components built in. KNIME integrates with Weka, another open-source project, which adds machine learning algorithms to the system. The R project adds statistical functionalities as well.

KNIME features the concept of a modular data pipeline, which allows for data mining within a straightforward user interface. Data preprocessing, modelling, analysis, and visualization are all enabled within KNIME.

The workflows can run both through the interactive interface and also in batch mode. These two setups allow for easy local job management and regular process execution.

One of the primary benefits of KNIME is the ability to create visual data flows. Users can then selectively execute the steps of analysis and review the output with the interactive view. The core version of KNIME has hundreds of modules already incorporated. This means KNIME supports the common database management systems right out of the box.

All common methods for data analysis and visualization are already found in KNIME’s core version. This includes the ability to filter, convert, and combine data sets. However, certain extensions are extremely popular thanks to their added practical functionalities.

For instance, the Report Designer is a free extension that most KNIME users will install. With this extension, a workflow in KNIME can become a dataset, enabling the user to create a report template. This can then get exported into multiple formats.

Other extensions allow for text mining, image mining, time series analysis, and so on.

Benefits and Features

Some of the Collaborative Extensions of KNIME include TeamSpace, Server Lite, WebPortal, and the KNIME Server. The KNIME Analytics Platform in and of itself has over 1,000 routines for data analysis. Together, these allow for:

  • Univariate Statistics
  • Multivariate Statistics
  • Data Mining
  • Time Series
  • Image Processing
  • Web Analytics
  • Text Mining
  • Network Analysis
  • Social Media Analysis

Scalability is one of the key features that KNIME promises. With its countless extensions, there are many ways to customize and grow the

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system to fit a company’s specific needs.

The intuitive user interface also helps speed up the learning curve. In fact, considering the many possibilities of the KNIME system, the interface makes everything quite easy to use. Being able to import and export workflows also gives way for collaboration between multiple KNIME users.

For an environment that runs a multi-core system, features like parallel execution will prove extremely valuable. Add to this the capability of “headless” batch executions using the command line version and it’s easy to see why many prefer KNIME.

Parallel execution in Knime is an extremely helpful feature!
The many modules that are available for KNIME users cover just about every functionality you could image.I/O is a popular one, which is able to retrieve data from files or data bases.
  • Data Manipulation is another frequently used module. It can pre-process your data with the ability to filter, group, pivot, bin, normalize, aggregate, join, sample, partition, and so on.
  • Views allows a user to inspect data and analysis results using multiple views. This gives a means for truly interactive exploration of a data set.
  • Mining makes use of multiple algorithms (like clustering and neural networks) to help a user better understand their data.
  • Hiliting keeps hilited data points hilited in every view to maintain accuracy.

The full list of available modules is simply too long to get into. There are literally hundreds to consider.

The KNIME system functions on multiple operating systems. The Windows 32bit version stays up-to-date with XP and Vista operating systems. The Windows 64bit version stays up-to-date with Vista and all machines under Windows 7.

It also operates with multiple Linux systems, including RHEL4/5 and OpenSUSE 10.2/10.3/11.0. KNIME 2.1 works on Mac OSX as well. A preliminary version is also available for Mac OSX that requires Java 1.6.

What Users Think

Gartner Peer Insights features 85 ratings for the KNIME analytics platform, averaging 4.6 stars out of 5. One review states:

“The KNIME analytics platform (and the server, for specific use cases) satisfies all of our needs and then some. In fact, new functionality sometimes opens up new possibilities that we had not previously considered – an example of this is the local big data environment functionality, making the use of SPARK far easier to experiment with.”

Users consistently highlight the support as one of the major benefits of using the KNIME platform. They also note that it’s easy to use with continuous updates. The multiple integrations is definitely a huge perk of this open source platform. Updates and modules are constantly released to extend functionality even further.

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