Difference between revisions of "Data Mining Facilities"

Latest revision as of 00:09, 19 June 2023

Overview

Data Mining facilities include a set of features, services and methods for performing data processing and mining on information sets. These features face several aspects of data processing ranging from modeling to clustering, from identification of anomalies to detection of hidden series. 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

Specifications

DataMiner

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

How-to Implement Algorithms for DataMiner

How to implement algorithms for DataMiner

Statistical Algorithms Importer

a tool to import processes on DataMiner

DataMiner Installation

Installation guide for DataMiner

How to Interact with the DataMiner by client

Interacting with DataMiner from a thin client

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 previous gCube system for Cloud computing

DataMiner Pool Manager

Automatic installer system of algorithms

Current usage statistics

Data extracted using the gCube accounting system.

Overall number of process requests per month

~64 300

Nr. of Algorithms integrated

~600

Overall number of users

~3000

Availability

99.7%

Last Update: June. 2023