Data Consumption Facilities
gCube Data Consumption facilities are dedicated to provide its users with a rich array of services for the exploitation of data in the context of the whole infrastructure ranging from the retrieval to the processing and visualisation.
gCube provides Information Retrieval facilities over large heterogeneous environments. Sources of information that use different technologies, data representation and semantics can be integrated and exploited by gCube's Data Retrieval framework. The architecture and mechanisms provided by the framework ensure flexibility, scalability, high performance and availability. For further information, please see this article.
gCube provides Data Manipulation Facilities responsible for transforming content and metadata among different formats and specifications. The architecture and mechanisms provided by the framework satisfy the requirements for arbitrary transformation or homogenization of content and metadata. For further information, please see this article.
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. For further information, please see this article.
Data Visualisation facilities include a set of features, software and methods for performing visualisation of data stored in the D4Science e-Infrastructure. Data Visualisation is particularly meant for geo-spatial data, which is a kind of information that naturally lends to visualisation. Data are reproduced on interactive maps and can be explored by means of several inspection tools. The adopted paradigm for maps visualisation needs to query a central GeoNetwork instance that indexes several geo-spatial data sources. For further information, please see this article.
Semantic Data Analysis
This task aims to deliver a set of libraries and services to bridge the gap between communities and link distributed data across community boundaries. The introduction of the Semantic Web and the publication of expressive metadata in a shared knowledge framework enable the deployment of services that can intelligently use Web resources. For further information, please see this article.