Objective
Over the last decade or so, the size of machine-readable data sets has increased dramatically and the problem of "data explosion" has become apparent. However, in paralell with this, recent developments in computing have provided the basic infrastructure for fast access to vast amounts of online data; at the same time many advanced computational methods for extracting information from large quantities of heterogeneous data and for data representation are beginning to mature. These developments have created a new range of problems and challenges for the analysts, as well as new opportunities for intelligent systems in data analysis. All this has led to the emergence of the field of Intelligent data Analysis (IDA), a combination of diverse disciplines including Artificial Intelligence and Statistics in particular.
These disciplines often complement each other: many statistical methods, particularly those for large data sets; rely on computation; but brute computing power is no substitute for statistical knowledge. Although many interesting systems and applications have been developed in the field, much more needs to be done. For instance, most of the data collected so far have not been analysed yet, and there are only a few tools around which allow the effective analysis of big data. Different analysis strategies may be applied to the same problem and it is often difficult to judge which is the most appropriate; also the division of the work between the computer and the analyst most effectively is by and large still work of art. The summer school on Intelligent Data Analysis will give an overview of all different disciplines for modern data analysis and of their practical and theoretical inter-relationships.
ftp://ftp.cordis.lu/pub/improving/docs/HPCF-2000-00401-1.pdf(opens in new window)
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence
- natural sciences computer and information sciences data science big data
- natural sciences computer and information sciences computational science
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
Italy
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.