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 is showcased in two pilot application areas: systems biology and digital multimedia repositories.
RBI (Rudjer Boskovic Institute) group from Division of electronics leads activities in Workpackage 13: Personalization and recommendation services for digital multimedia repositories.
- Project category
- Sedmi okvirni program za razvoj i istraživanje
- Project leader / principal investigator
- Ugovoreni iznos financiranja
- 191,680 EUR
Dragan Gamberger (senior scientist)
Nino Antulov-Fantulin (PhD student)
Matija Piškorec (PhD student)
Matko Bošnjak (PhD student)
Matej Mihelčić (PhD Student)
- Commercial potential
- modern recommender systems technologies
- building data mining and recommendation services
- Project start date
- Project end date
- Project URL