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Completed research project

Title / Titel e-LICO - An e-Laboratory for Interdisciplinary Collaborative Research in Data Mining and Data-Intensive Sciences
PDF Abstract (PDF, 14 KB)
Summary / Zusammenfassung The goal of the e-LICO project is to build a virtual laboratory for interdisciplinary collaborative research in data mining and data-intensive sciences. The proposed e-lab will comprise three layers: the e-science and data mining layers will form a generic research environment that can be adapted to different scientific domains by customizing the application layer. The e-science layer, built on an open-source e-science infrastructure developed by one of the partners, will support content creation through collaboration at multiple scales and degrees of commitment—ranging from small, contract-bound teams to voluntary, constraint-free participation in dynamic virtual communities. The data mining layer will be the distinctive core of e-LICO; it will provide comprehensive multimedia (structured records, text, images, signals) data mining tools. Standard tools will be augmented with preprocessing or learning algorithms developed specifically to meet challenges of data-intensive, knowledge rich sciences, such as ultra-high dimensionality or undersampled data. Methodologically sound use of these tools will be ensured by a knowledge-driven data mining assistant, which will rely on a data mining ontology and knowledge base to plan the mining process and propose ranked workflows for a given application problem. Extensive e-lab monitoring facilities will automate the accumulation of experimental meta-data to support replication and comparison of data mining experiments. These meta-data will be used by a meta-miner, which will combine probabilistic reasoning with kernel-based learning from complex structures to incrementally improve the assistant's workflow recommendations. e-LICO will be showcased in a systems biology task: biomarker discovery and molecular pathway modelling for diseases affecting the kidney and urinary pathways.
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Publications / Publikationen Alexandros Kalousis, Abraham Bernstein, Melanie Hilario, Meta-learning with kernels and similarity functions for planning of data mining work?ows, Proceedings of the ICML/COLT/UAI 2008 Planing to Learn Workshop, Editor(s): Pavel Brazdil, Abraham Bernstein, Larry Hunter, July ; 2008.

Abraham Bernstein, Michael Daenzer, The NExT System: Towards True Dynamic Adaptions of Semantic Web Service Compositions (System Description), Proceedings of the 4th European Semantic Web Conference (ESWC '07), March 2007, Springer.

Abraham Bernstein, Foster Provost, Shawndra Hill, Towards Intelligent Assistance for a Data Mining Process: An Ontology-based Approach for Cost-sensitive Classification, IEEE Transactions on Knowledge and Data Engineering Vol. 17 (4), April 2005.

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Project leadership and contacts /
Projektleitung und Kontakte
Prof. Abraham Bernstein, PhD (Project Leader)
Funding source(s) /
Unterstützt durch
In collaboration with /
In Zusammenarbeit mit
Melanie Hilario, University of Geneva Switzerland
Robert Stevens, University of Manchester United Kingdom
Rapid-I GmBH plus
- Institut National de la Santé et de la Recherche Médicale (France)
- Medicel Oy (Finland)
- National Hellenic Research Foundation (Greece)
Duration of Project / Projektdauer Feb 2009 to Feb 2012