Difference between revisions of "Data Mining Facilities"

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;[[Signal Processing]]
;[[Signal Processing]]
: a set of methods to perform digital signal processing.
: a set of methods to perform digital signal processing.
;[[Statistical_Manager_Tutorial | Statistical Manager]]
: the former gCube system for Cloud computing

Revision as of 03:19, 24 November 2016


Data Mining facilities include a set of features, services and methods for performing data processing and mining on biological information sets. These features face several aspects of biological data processing ranging from ecological modeling to niche modeling experiments. This set of services and libraries is used by the D4Science e-infrastructure to manage data mining problems even from a computational complexity point of view. Algorithms are executed in parallel and possibly distributed fashion, using the same D4Science nodes as working nodes. Furthermore, Services performing Data Mining operations are deployed according to a distributed architecture, in order to balance the load of those procedures requiring local resources.

By means of the above features, Data Mining aims to manage problems like (i) the prediction of the impact of climate changes on biodiversity, (ii) the prevention of the spread of invasive species, (iii) the identification of geographical and ecological aspects of disease transmission, (iv) the conservation planning, (v) the prediction of suitable habitats for marine species. By using the computational facilities of the D4Science e-Infrastructure, algorithms can run in a cost-effective way letting scientists perform more experiments and combine different techniques.

Key Features

The components part of the subsystem provide the following main key features:

parallel processing
parallelization of statistical algorithms using a map-reduce approach
cloud computing approach in a seamless way to the users
pre-cooked state-of-the-art data mining algorithms
algorithms oriented to biological-related problems supplied as-a-service
general purpose algorithms (e.g. Clustering, Principal Component Analysis, Artificial Neural Networks) supplied as-a-service
data trends generation and analysis
extraction of trends for biodiversity data
inspection of time series of observations on biological species
basic signal processing techniques to explore periodicities in trends
ecological niche modelling
algorithms to perform ecological niche modelling using either mechanistic or correlative approaches
species distribution maps generation


a Service allowing the management of statistical data and multi-user requests for computation
DataMiner Algorithms
the complete list of algorithms supported by the DataMiner
Statistical Algorithms Importer
a tool to import R processes on DataMiner
Ecological Modeling
a set of methods for performing Data Mining operations. These include experiments and techniques categorization
Signal Processing
a set of methods to perform digital signal processing.
Statistical Manager
the former gCube system for Cloud computing