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Towards cooperative planning of data mining workflows


Kietz, J-U; Serban, F; Bernstein, A; Fischer, S (2009). Towards cooperative planning of data mining workflows. In: Proc of the ECML/PKDD09 Workshop on Third Generation Data Mining: Towards Service-oriented Knowledge Discovery (SoKD-09), Bled, Slovenia, September 2009 - September 2009.

Abstract

A major challenge for third generation data mining and knowledge discovery systems is the integration of different data mining tools and services for data understanding, data integration, data preprocessing, data mining, evaluation and deployment, which are distributed across the network of computer systems. In this paper we outline how an intelligent assistant that is intended to support end-users in the difficult and time consuming task of designing KDD-Workflows out of these distributed services can be built. The assistant should support the user in checking the correctness of workflows, understanding the goals behind given workflows, enumeration of AI planner generated workflow completions, storage, retrieval, adaptation and repair of previous workflows. It should also be an open easy extendable system. This is reached by basing
the system on a data mining ontology (DMO) in which all the services (operators) together with their in-/output, pre-/postconditions are described. This description is compatible with OWL-S and new operators can be added importing their OWL-S specification and classifying it into
the operator ontology.

A major challenge for third generation data mining and knowledge discovery systems is the integration of different data mining tools and services for data understanding, data integration, data preprocessing, data mining, evaluation and deployment, which are distributed across the network of computer systems. In this paper we outline how an intelligent assistant that is intended to support end-users in the difficult and time consuming task of designing KDD-Workflows out of these distributed services can be built. The assistant should support the user in checking the correctness of workflows, understanding the goals behind given workflows, enumeration of AI planner generated workflow completions, storage, retrieval, adaptation and repair of previous workflows. It should also be an open easy extendable system. This is reached by basing
the system on a data mining ontology (DMO) in which all the services (operators) together with their in-/output, pre-/postconditions are described. This description is compatible with OWL-S and new operators can be added importing their OWL-S specification and classifying it into
the operator ontology.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:September 2009
Deposited On:04 Feb 2010 11:37
Last Modified:05 Apr 2016 13:39
Permanent URL: https://doi.org/10.5167/uzh-25868

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