Citation
Mai Nguyen, Daniel Crawl, Jianwu Wang, Ilkay Altintas. "Machine Learning Module for Big Data Analysis in Kepler". Talk or presentation, 16, October, 2015; Presented at the Eleventh Biennial Ptolemy Miniconference, Berkeley.

Abstract
Kepler is a scientific workflow system that is built on the Ptolemy II framework. Kepler provides a graphical user interface that allows users to easily design scientific workflows by simply dragging and dropping actors implementing different operations and linking them together to create the steps necessary for a specific workflow. Machine learning techniques provide a way to analyze the problem being studied using a data-driven approach, and is an essential part of many scientific processes. The machine learning module in Kepler allows users to integrate machine learning functionality into a workflow, even if the machine learning algorithms are implemented on different tools or platforms. For example, an R script can be executed within Kepler using the RExpression actor. A Mahout algorithm or KNIME workflow can be executed in command-line mode using the ExternalExecution actor. For big data processing, actors implementing Spark MLLib algorithms are being developed in Kepler. Spark is a cluster computing framework, and MLlib is a distributed machine learning library on top of Spark. Spark’s distributed in-memory architecture provides fast and scalable processing of iterative operations, which is ideal for machine learning algorithms. The machine learning module in Kepler can also create an actor for a single machine learning algorithm based on different implementations. As an example, the kmeans-all actor in Kepler implements the k-means clustering algorithm using R, Spark MLlib, Mahout, and KNIME. This feature allows the user to compare accuracy and processing results for a single algorithm using different implementations. This can be accomplished using the same actor, with the only change being the choice of implementation when the actor is executed. The user does not need to know much about any of the underlying tools (e.g., R or Spark) in order to use these actors. Each actor in the machine learning module can also be connected to other actors available in Kepler to build complex workflows.

Electronic downloads

• Nguyen_ML4BigDataAnalysisInKepler_PtolemyMiniConference_16Oct2015.pptx · application/vnd.openxmlformats-officedocument.presentationml.pre · 1538 kbytes

• HTML

  Mai Nguyen, Daniel Crawl, Jianwu Wang, Ilkay Altintas. <a
  href="http://chess.eecs.berkeley.edu/pubs/1123.html"><i>Machine
  Learning Module for Big Data Analysis in
  Kepler</i></a>, Talk or presentation,  16,
  October, 2015; Presented at the <a
  href="http://ptolemy.eecs.berkeley.edu/conferences/15/"
  >Eleventh Biennial Ptolemy Miniconference</a>,
  Berkeley.

• Plain text

  Mai Nguyen, Daniel Crawl, Jianwu Wang, Ilkay Altintas.
  "Machine Learning Module for Big Data Analysis in
  Kepler". Talk or presentation,  16, October, 2015;
  Presented at the <a
  href="http://ptolemy.eecs.berkeley.edu/conferences/15/"
  >Eleventh Biennial Ptolemy Miniconference</a>,
  Berkeley.

• BibTeX

  @presentation{NguyenCrawlWangAltintas15_MachineLearningModuleForBigDataAnalysisInKepler,
      author = {Mai Nguyen and Daniel Crawl and Jianwu Wang and
                Ilkay Altintas},
      title = {Machine Learning Module for Big Data Analysis in
                Kepler},
      day = {16},
      month = {October},
      year = {2015},
      note = {Presented at the <a
                href="http://ptolemy.eecs.berkeley.edu/conferences/15/"
                >Eleventh Biennial Ptolemy Miniconference</a>,
                Berkeley},
      abstract = {Kepler is a scientific workflow system that is
                built on the Ptolemy II framework. Kepler provides
                a graphical user interface that allows users to
                easily design scientific workflows by simply
                dragging and dropping actors implementing
                different operations and linking them together to
                create the steps necessary for a specific
                workflow. Machine learning techniques provide a
                way to analyze the problem being studied using a
                data-driven approach, and is an essential part of
                many scientific processes. The machine learning
                module in Kepler allows users to integrate machine
                learning functionality into a workflow, even if
                the machine learning algorithms are implemented on
                different tools or platforms. For example, an R
                script can be executed within Kepler using the
                RExpression actor. A Mahout algorithm or KNIME
                workflow can be executed in command-line mode
                using the ExternalExecution actor. For big data
                processing, actors implementing Spark MLLib
                algorithms are being developed in Kepler. Spark is
                a cluster computing framework, and MLlib is a
                distributed machine learning library on top of
                Spark. Spark’s distributed in-memory
                architecture provides fast and scalable processing
                of iterative operations, which is ideal for
                machine learning algorithms. The machine learning
                module in Kepler can also create an actor for a
                single machine learning algorithm based on
                different implementations. As an example, the
                kmeans-all actor in Kepler implements the k-means
                clustering algorithm using R, Spark MLlib, Mahout,
                and KNIME. This feature allows the user to compare
                accuracy and processing results for a single
                algorithm using different implementations. This
                can be accomplished using the same actor, with the
                only change being the choice of implementation
                when the actor is executed. The user does not need
                to know much about any of the underlying tools
                (e.g., R or Spark) in order to use these actors.
                Each actor in the machine learning module can also
                be connected to other actors available in Kepler
                to build complex workflows. },
      URL = {http://chess.eecs.berkeley.edu/pubs/1123.html}
  }
  

Posted by Christopher Brooks on 19 Oct 2015.
Groups: ptolemy
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