This paper introduces the first ontological modeling environment for planning Knowledge Discovery (KDD) workflows. We use ontological reasoning combined with AI planning techniques to automatically generate workflows for solving Data Mining (DM) problems. The KDD researchers can easily model not only their DM and preprocessing operators but also their DM tasks, that are used to guide the workflow generation

Bernstein, A

Kietz, J U

Serban, F

ZORA

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This paper introduces the first ontological modeling en-vironment for planning Knowledge Discovery (KDD) workflows.We use ontological reasoning combined with AI planning techniquesto automatically generate workflows for solving Data Mining (DM)problems. The KDD researchers can easily model not only their DMand preprocessing operators but also their DM tasks, that are used toguide the workflow generation.1 IntroductionCurrent DM-Suites support the user only with the manual creation ofKDD workflows, but this is time consuming and requires experienceand knowledge about the DM operators as well as the DM-Suites.Over the course of time several people have tried to build systems thatcan automate this process[1, 2, 11]. These approaches have shownthat AI planning is a way of supporting the user with automaticallygenerated workflows. But modeling for planning is difficult as wellsince one has to describe the DM domain in terms of operations thatcan be applied. To our knowledge none of these systems contain amodel of the operators of a state-of-the-art DM-Suites nor are theypublicly available.In this paper we present eProPlan2, the first ontology-based In-tegrated Development Environment (IDE) for planning applica-tions. Together with the DMWF-DMO (Data Mining Work Flow-Ontology) and over 100 modeled RapidMiner operators it is the firstpublicly available planner for planning DM workflows. We also de-fined a programming interface (IDA-API), such that generated work-flows can be delivered directly to DM-Suites like RapidMiner or gen-eral Service-Workflow engines like Taverna [6].In Section 2 we present the architecture of our system, Section 3describes the steps and audience of the demonstration, we concludewith a short discussion of innovations of eProPlan over related workin Section 4.2 eProPlan ArchitectureThe system comprises several Prote´ge´ 4 [9] plugins, that allow theusers to describe and solve DM problems. By using the public-domain ontology-editor Prote´ge´ 4 as the base environment we ex-ploit the advantages of an ontology as a formal model for the domain1 University of Zurich, Department of Informatics, Dynamic and Dis-tributed Information Systems Group, Binzmu¨hlestrasse 14, CH-8050Zurich, Switzerland {kietz|serban|bernstein}@ifi.uzh.ch2 eProPlan, DMWF-DMO and RapidMiner-operator ontology can be down-loaded from http://www.e-lico.eu/eProPlan. The IDA-API and its integra-tion into RapidMiner will be available soon. For a first impression this sitealso contains screen-shoots and preliminary demo videos.knowledge. But instead of over-using the ontological inferences forplanning (as done in [3, 12]) we decided to extend the ontologicalformalism with the main components of a plan, namely operator con-ditions & effects for classic planning and tasks & methods for Hier-archical Task Network (HTN) planning [10].DMO is our DM-Ontology. It consists of two main parts: TheDMWF [7] which contains IO-objects, operators, goals, tasks andmethods as well as the decomposition of tasks into methods and oper-ators. The DMWF is used by the AI-planner. The DMOP [4] focuseson the operators’ features and is used by the probabilistic-planner tooptimize the plans generated by the AI-planner.eProPlanI is our reasoner plugin, i.e. the interface of our reasoner& planner for Prote´ge´. It combines ontological reasoning and HTN-planning. Other plugins (like eProPlanP) rely on it since they retrieveand display useful information about the DM process (operators andtheir applicability). eProPlanO is an editor for operators. Operatorshave conditions and effects expressed in an extended SWRL lan-guage [5] as shown in [7]. Our customized tab displays for each oper-ator its conditions and effects (both current and inherited), only cur-rent conditions and effects can be edited. We extended the SWRL ed-itor from Prote´ge´ 4 and built our own editor that has a syntax checkerand a variable binding checker.eProPlanM provides the support for HTN modeling as atask/method editor, users can define their own task/method decom-positions. Methods and tasks’ modeling – consists of three steps andis managed by a customized tab with three class views. The first viewpresents a tree with the decomposition of tasks into methods, meth-ods into tasks and operators classes. The user can easily add/ deletenew tasks and methods as well as specify their decomposition intosubsteps. The next view displays the conditions and contributionsfor methods. The third view shows the method bindings for a se-lected method.eProPlanG represents a DM-task specification editor. It enables theuser to specify the input data as well as describe the DM-task shewants to solve in terms of main goals and optional subgoals the plan-ner has to reach.eProPlanP is the support for workflow execution and visualization.It has mainly two functionalities: provides the user a step-by-stepplanner – it displays applicable operators for a data set and can applythem, and shows all the plans for a specific DM task.IDA-API is the programming interface to the reasoner & plannerused to build Intelligent Discovery Assistants (IDA) based on theservices of the planner. Within the e-Lico project Rapid-I is currentlyusing this API to integrate the planner into the leading open-sourceDM-Suite RapidMiner, such that RapidMiner users can get automat-ically generated DM workflows to execute them there.Modeling & testingReasoning & planningWorkflow generation IDA-API!"#!"#$%&'"()$*(!"#$%+"$,*-.%!"#$%/0)1#2"$(3"4"%/0)12"')3(%/0)156'0)31%/0)17)03-)$"%/0)18''09%&'"()$*(8''03:);0"%&'"()$*(#.%/0)1#%<*(%=)#>=)#>%56')1#3*1#56')1-%=)#>Figure 1: (a) eProPlan architecture (b) The services of the plannerThe complete system will have the services displayed in Figure 1bwhich are developed as part of the e-Lico project. Currently the AIplanner’s services are available, the other two parts are under devel-opment.3 DemonstrationThe demonstration will cover the whole life-cycle of DM-workflowplanning frommodeling data sets, preprocessing- and DM-operators,DM-goals and task/method decompositions, via testing the model ineProPlan by entering specific goals and getting the DMWF-meta-data description of concrete data sets from a data analysis service,to the generation of complete DM-workflows within eProPlan andwithin RapidMiner, where the generated workflows can be executed.The demo is of interest for the KDD researchers as well as for theKDD practitioners. Researchers may want to model their own opera-tors to be used in generated workflows. An advanced usage scenariois that researchers model their own task/method decompositions, e.g.to let the planner generate systematic experiments to be executed bya DM-Suite. DM-Suite developers may be interested in the servicesthe system can add to their suite. Practitioners can get an impressionof how future systems can support them in their daily work of doingDM projects. An extended description of the system and how it isused for auto-experimentation is available in [8].4 ConclusionsThere are many attempts at automating the generation of KDD work-flows: Zˇa´kova´ et. al [12] automatically generate workflows using aknowledge ontology and a planning algorithm based on the Fast-Forward system. However, they only return the shortest workflowwith the smallest number of processing steps – no alternatives aregenerated. IDEA [1], the Intelligent Discovery Assistant (IDA), pro-vides users with systematic enumerations of valid DM processes. Itis based on an ontology of DM operators that guides the workflowcomposition and contains heuristics for the ranking of different alter-natives. CITRUS [11] consists of an IDA which offers user-guidancethrough mostly a manual process of building the workflows. It usesplanning for plan decomposition and refinement.Similar to IDEA our system also provides many plans. We believethat this is a necessary condition as the system may not know allthe tradeoffs between operators and/or the user’s desiderata. Also,as far as we know, ePropPlan is the first and only KDD workflowplanner that combines this ability with an integrated Planning De-velopment Environment, can be easily integrated into existing DMsuites through its IDA-API, as it is publicly available.Acknowledgements: This work is supported by the European Com-munity 7th framework ICT-2007.4.4 (No 231519) “e-Lico: An e-Laboratory for Interdisciplinary Collaborative Research in Data Min-ing and Data-Intensive Science”.REFERENCES[1] Abraham Bernstein, Foster Provost, and Shawndra Hill, ‘Towards Intel-ligent Assistance for a Data Mining Process: An Ontology-based Ap-proach for Cost-sensitive Classification’, IEEE Transactions on Knowl-edge and Data Engineering, 17(4), 503–518, (2005).[2] M. Charest, S. Delisle, O. Cervantes, and Y. Shen, ‘Bridging the gapbetween data mining and decision support: A case-based reasoning andontology approach’, Intelligent Data Analysis, 12(2), 211–236, (2008).[3] Claudia Diamantini, Domenico Potena, and Emanuele Storti, ‘KD-DONTO: An Ontology for Discovery and Composition of KDD Al-gorithms’, in Service-oriented Knowledge Discovery (SoKD-09) Work-shop at ECML/PKDD09, (2009).[4] Melanie Hilario, Alexandros Kalousis, Phong Nguyen, and AdamWoznica, ‘A data mining ontology for algorithm selection and meta-learning’, in Service-oriented Knowledge Discovery (SoKD-09) Work-shop at ECML/PKDD09, (2009).[5] I. Horrocks, P.F. Patel-Schneider, H. Boley, S. Tabet, B. Grosof, andM. Dean, SWRL: A Semantic Web Rule Language Combining OWL andRuleML, http://www.w3.org/Submission/SWRL/, 2004.[6] D. Hull, K. Wolstencroft, R. Stevens, C. Goble, M.R. Pocock, P. Li,and T. Oinn, ‘Taverna: a tool for building and running workflows ofservices’, Nucleic acids research, 34(Web Server issue), W729, (2006).[7] J.-U. Kietz, F. Serban, A. Bernstein, and S. Fischer, ‘Towards cooper-ative planning of data mining workflows’, in Service-oriented Knowl-edge Discovery (SoKD-09) Workshop at ECML/PKDD09, (2009).[8] J.-U. Kietz, F. Serban, A. Bernstein, and S. Fischer, ‘Data min-ing workflow templates for intelligent discovery assistance and auto-experimentation’, Technical report, University of Zu¨rich, (06 2010).Available at http://www.e-lico.eu/public/SoKD10.pdf.[9] H. Knublauch, R.W. Fergerson, N.F. Noy, and M.A. Musen, ‘TheProte´ge´ OWL plugin: An open development environment for semanticweb applications’, Lecture notes in computer science, 229–243, (2004).[10] D. Nau, T.-C. Au, O. Ilghami, U. Kuter, W. Murdock, D. Wu, andF.Yaman., ‘SHOP2: An HTN planning system.’, JAIR, 20, 379–404,(2003).[11] R. Wirth, C. Shearer, U. Grimmer, T. Reinartz, J. Schlo¨sser, C. Breit-ner, R. Engels, and G. Lindner, ‘Towards process-oriented tool supportfor knowledge discovery in databases’, Principles of Data Mining andKnowledge Discovery, 243–253, (1997).[12] M. Zˇa´kova´, V. Podpecˇan, F. Zˇelezny´, and N. Lavracˇ, ‘Advancing datamining workflow construction: A framework and cases using the orangetoolkit’, in Service-oriented Knowledge Discovery (SoKD-09) Work-shop at ECML/PKDD09, (2009).

eProPlan: a tool to model automatic generation of data mining workflows

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eProPlan: a tool to model automatic generation of data mining workflows

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