e-Laboratory for Interdisciplinary Collaborative Research in Data Mining and Data-Intensive Sciences
e-Laboratorij za skupinske interdisciplinarne raziskave na področju rudarjenja podatkov in podatkovno intenzivnih znanosti

No. of contract:

Type of project:
from 01.06.2010 to 31.01.2012

Intelligent Content and Semantics
e-LICO offers a data mining lab to scientists struggling to analyse massive data spawned by high-throughput technologies

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 comprises three layers: the e-science layer and the data mining layer form a generic knowledge discovery platform that can be adapted to different scientific domains by customizing the application layer. The project’s overall research strategy can be summarized as the bottom-up construction of this three-tiered architecture.

The foundation of the e-science layer is a suite of open-source components developed by the University of Manchester (e.g. myGrid e-science platform, Taverna workflow editor); these components will be extended with tools for content creation (e.g. semantic annotation, ontology engineering) as well as mechanisms for multiple levels and modes of collaboration in experimental research.

The data mining layer is the distinctive core of e-LICO; it will provide a comprehensive set of multimedia (structured records, text, images, signals) data mining tools. Standard tools will be complemented with preprocessing or learning algorithms developed specifically to respond to problems of data-intensive, knowledge rich sciences, such as extremely high dimensionality and undersampling, learning from heterogeneous data, incorporating prior knowledge into learning. Methodologically sound use of these tools will be ensured by a knowledge-driven, planner-based data mining assistant, which will rely on a data mining ontology to plan the data mining process and propose ranked workflows for a given application problem. Extensive e-lab monitoring facilities will support comparison and analysis of experiments by a meta-miner, which will combine probabilistic reasoning with kernel-based learning to incrementally improve the assistant’s workflow recommendations.

The application layer is always domain-specific. In the generic e-lab, the application layer is an empty shell. It is built by the domain user who will use the tools available in the e-science and DM layers to access available services and resources (e.g. knowledge bases, ontologies) or develop new ones; design, run and analyse data mining workflows; and semantically annotate experimental data as well as mined models in domain-specific terms.

The data mining e-lab will be showcased on a systems biology task: biomarker discovery and pathway modelling for diseases affecting the kidney and urinary pathways (KUP). Domain-specific knowledge sources, such as a specialized ontology and a data base on KUP-related diseases will be collaboratively authored by European specialists in the area. Multi-omic (e.g. genomic, transcriptomic, proteomic, metabolomic) data provided by biologists and clinicians gathered in COST Action BM0702 (EuroKUP) will be mined and the resulting diagnostic/prognostic models made available in a repository of data mining experiments.

The final deliverable of the project will be a free, experimental prototype open to continuous collaborative expansion and refinement by the research community.