Project details for ClowdFlows

Logo ClowdFlows 0.9

by janezkranjc - October 8, 2013, 02:57:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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ClowdFlows is an open sourced cloud based platform for composition, execution, and sharing of interactive machine learning and data mining workflows. It is based on the principles of service-oriented knowledge discovery and features interactive scientific workflows. In contrast to comparable data mining platforms, ClowdFlows runs in all major Web browsers and platforms. ClowdFlows provides researchers with an easy way to expose and share their work and results, as only an Internet connection and a Web browser are required to access the workflow from anywhere. Practictioners can use ClowdFlows to seamlessly integrate and join different implementations of algorithms, tools and Web services into a coherent workflow that can be executed in a cloud based application. ClowdFlows is also easily extensible during run-time by importing Web services and using them as new workflow components.

Another key feature of the ClowdFlows platform is its stream mining module. By use of two special mechanisms workflows can be transformed into streaming workflows that are executed indefinitely and constantly mine data on the input streams while intermediate results can be accessed by directing a browser to a particular URL.

ClowdFlows is actively developed at the Department of Knowledge Technologies of the Jo┼żef Stefan Institute, Ljubljana, Slovenia.

ClowdFlows is publicly deployed at where anyone can sign up and start constructing and executing workflows. However the source code can also be downloaded and a private version of ClowdFlows can easily be setup.

For installation and usage instructions please check out the wiki at our github page:

Clowdflows wiki

ClowdFlows github repo

Live installation of ClowdFlows

Changes to previous version:

Initial Announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Agnostic
Tags: Machine Learning, Data Mining, Workflow, Webservice, Big Data, Cloud, Stream Mining
Archive: download here


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