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The I-BiDaaS project aims to empower end-users to utilize and interact with big data technologies more easily.

This innovation corresponds to specification of an end-to-end platform for Big Data as-a-self-service, developed within the I-BiDaaS project.

This is an open-source implementation of the Alternating Direction Method of Multipliers (ADMM)[1] optimization algorithm that relies on CVXPY[2], a Python-based toolbox for

Our technology allows organizing the data according to their multidimensional attributes while building stratified samples at high-speed.

SOFIA DSS is a model and data-driven system that addresses the challenge of personnel evaluation and recruitment.

The Language Processing use case will focus on processing large amounts of messages from social media, such as Twitter, in order to perform semantic information extraction, sentiment analysis, summarization, interpretation and organization of content.

BigDataStack will deliver a complete high-performance data-centric stack of technologies as a unique combined and cross-optimized offering that addresses the emerging needs of data operations and applications.

The AEGIS platform provides a multi-tenant data management and processing services for big data. The multi-tenancy behaviour allows different users and services to securely and privately access and process their data.

The goal of the DataBench Toolbox is to provide a way of reusing existing big data benchmarking efforts under a common framework, providing therefore a way to select, download and homogenize technical and business indicators.

The all-in-one data orchestration solution discovers, refines, enriches, and governs any type, variety, and volume of data across your entire distributed data landscape.