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Designing KDD-Workflows via HTN-Planning


Kietz, Jörg-Uwe; Serban, Floarea; Bernstein, Abraham; Fischer, Simon (2012). Designing KDD-Workflows via HTN-Planning. In: European Conference on Artificial Intelligence, Systems Demos, Montpellier, France, 27 August 2012 - 31 August 2012. I O S Press, 1011-1012.

Abstract

Knowledge Discovery in Databases (KDD) has evolved a lot during the last years and reached a mature stage offering plenty of operators to solve complex data analysis tasks. However, the user support for building workflows has not progressed accordingly. The large number of operators currently available in KDD systems makes it difficult for users to successfully analyze data. In addition, the correctness of workflows is not checked before execution. This demo presents our tools, eProPlan and eIDA, which solve the above problems by supporting the whole cycle of (semi-) automatic workflow generation. Our modeling tool eProPlan, allows to describe operators and build a task/method decomposition grammar to specify the desired workflows. Additionally, our Intelligent Discovery Assistant, eIDA, allows to place workflows into data mining (DM) suites or workflow engines for execution.

Abstract

Knowledge Discovery in Databases (KDD) has evolved a lot during the last years and reached a mature stage offering plenty of operators to solve complex data analysis tasks. However, the user support for building workflows has not progressed accordingly. The large number of operators currently available in KDD systems makes it difficult for users to successfully analyze data. In addition, the correctness of workflows is not checked before execution. This demo presents our tools, eProPlan and eIDA, which solve the above problems by supporting the whole cycle of (semi-) automatic workflow generation. Our modeling tool eProPlan, allows to describe operators and build a task/method decomposition grammar to specify the desired workflows. Additionally, our Intelligent Discovery Assistant, eIDA, allows to place workflows into data mining (DM) suites or workflow engines for execution.

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Additional indexing

Item Type:Conference or Workshop Item (Other), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:31 August 2012
Deposited On:07 Dec 2012 16:02
Last Modified:06 Mar 2024 14:12
Publisher:I O S Press
Series Name:Frontiers in Artificial Intelligence and Applications
Number:242
ISSN:0922-6389
ISBN:978-1-61499-097-0 (print) | 978-1-61499-098-7 (online)
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3233/978-1-61499-098-7-1011
Related URLs:http://www2.lirmm.fr/ecai2012/
Other Identification Number:merlin-id:7117
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)