1,439,832 research outputs found
Projective Techniques and Functional Integration
A general framework for integration over certain infinite dimensional spaces
is first developed using projective limits of a projective family of compact
Hausdorff spaces. The procedure is then applied to gauge theories to carry out
integration over the non-linear, infinite dimensional spaces of connections
modulo gauge transformations. This method of evaluating functional integrals
can be used either in the Euclidean path integral approach or the Lorentzian
canonical approach. A number of measures discussed are diffeomorphism invariant
and therefore of interest to (the connection dynamics version of) quantum
general relativity. The account is pedagogical; in particular prior knowledge
of projective techniques is not assumed. (For the special JMP issue on
Functional Integration, edited by C. DeWitt-Morette.)Comment: 36 pages, latex, no figures, Preprint CGPG/94/10-
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Challenges of ultra large scale integration of biomedical computing systems
The NCRI Informatics Initiative is overseeing the implementation of an informatics
framework for the UK cancer research community. The framework advocates an integrated
multidisciplinary method of working between scientific and medical communities. Key to this
process is community adoption of high quality acquisition, storage, sharing and integration of
diverse data elements to improve knowledge of the causes, prevention and treatment of
cancer. The integration of the complex data and meta-data used by these multiple
communities is a significant challenge and there are technical, resource-based and
sociological issues to be addressed. In this paper we review progress aimed at establishing
the framework and outline key challenges in ultra large scale integration of biomedical
computing systems
Combining expert knowledge and databases for risk management
Correctness, transparency and effectiveness are the principalattributes of knowledge derived from databases. In current data miningresearch there is a focus on efficiency improvement of algorithms forknowledge discovery. However important limitations of data mining canonly be dissolved by the integration of knowledge of experts in thefield, encoded in some accessible way, with knowledge derived formpatterns in the database. In this paper we will in particular discussmethods for combining expert knowledge and knowledge derived fromtransaction databases.The framework proposed is applicable to widevariety of risk management problems. We will illustrate the method ina case study on fraud discovery in an insurance company.risk management;datamining;knowledge discovery;knowledge based systems
Coherent Integration of Databases by Abductive Logic Programming
We introduce an abductive method for a coherent integration of independent
data-sources. The idea is to compute a list of data-facts that should be
inserted to the amalgamated database or retracted from it in order to restore
its consistency. This method is implemented by an abductive solver, called
Asystem, that applies SLDNFA-resolution on a meta-theory that relates
different, possibly contradicting, input databases. We also give a pure
model-theoretic analysis of the possible ways to `recover' consistent data from
an inconsistent database in terms of those models of the database that exhibit
as minimal inconsistent information as reasonably possible. This allows us to
characterize the `recovered databases' in terms of the `preferred' (i.e., most
consistent) models of the theory. The outcome is an abductive-based application
that is sound and complete with respect to a corresponding model-based,
preferential semantics, and -- to the best of our knowledge -- is more
expressive (thus more general) than any other implementation of coherent
integration of databases
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