Data Intelligence

Helping to manage your data by researching on Big Data, AI, Semantics and Linked Data
Description: 

The Data Intelligence Lab researches on novel technologies in the fields of Big Data, Artificial Intelligence, Semantics and Linked Data. These complementary fields are amongst the technologies with more influence in the current business trends.

From companies to governments, from organizations to individuals, from the web to social networks, from traditional media to sensors, data is growing everywhere. Data is the new gold. Our lab is monitoring and researching on Big Data solutions to cope with this data deluge, trying to help all possible stakeholders to better acquire, store, organize, annotate, curate, analyze and finally use the data. We see Big Data as a philosophy, as a new paradigm that allows performing data analytics where nobody has gone before.

We deal with technologies related to the entire data value chain (data acquisition, pre-processing, analysis and usage). Of particular interest for us are the architectures, frameworks and techniques that are the foundations of any data-intensive related application. Big data analytics, with especial focus on text analytics using Language Technologies, is one of the key pillars of the work carried out within the lab.

On the other hand, the world is now in the quest of opening data to the public. Especially, but not only, the Public Sector is clearly embracing the open data initiative. Within the Data Intelligence Lab we research and apply the Linked Data paradigm to help organizations that need to share data on the web and at the same time offering a programmatic interface allowing not only humans, but also machines (programs) to get automatic access and understanding of the data. The use of semantics and Linked Data is a key enabler of the use of public data in the future.

Goals: 

The main objective is researching on technologies and their applicability related to data and meta-data management:

  • Big Data: Under the Big Data umbrella, the Data Intelligence Lab is particularly interested in pushing the state of the art in surfacing business intelligence from web resources and social networks, as well as investigating new solutions for big data storage, big data architectures, data visualization, data analytics and data science.
  • Artifiial Intelligence: From Machine Learning to Deep Learning to the multiple applications of the technology to real-world applications and challenges.
  • Linked Data: Application of the Linked Data paradigm for data publication and linking.
  • Semantics: Application of ontologies and language technologies for annotation, searching and extracting meaning from texts.
Main Activities: 

We believe there is no solution that fits-it-all, but general good architectural principles and best practices combined with an excellent knowledge of available tools and new research trends, make the difference between success and mediocrity.

  • We are working in projects and solutions for big data architectures, with special emphasis in bringing together innovative technologies in sounding architectures fit for specific purposes.
  • We are setting up and testing novel infrastructures for data acquisition and annotation, analyzing sentiments and bringing together semantics and big data.
  • We have developed a Social Network monitoring tool called Capturean that is the cornerstone of our knowledge transfer to commercialize research and innovation results.
  • We develop machine learning algorithms and tested deep learning techniques in the scope of data analytics projects.
  • We are trying to add our 2 cents to the Linked Open Data initiative by bringing Linked Data technologies in the scope of big data solutions, and our own developments to our projects, therefore promoting the uptake of open data.
  • We have an extensive track record in projects and solutions dealing with semantic technologies, such as ontology engineering, semantic applications for enterprises, natural language processing in English and Spanish, among others.
Challenges: 
  • Big Data
    • Architectural approaches to deal with massive amounts of historical and streaming data in a coherent manner.
    • Data acquisition from social networks, with special emphasis in gathering intelligence from Twitter.
    • Use of Cloud Computing for storage and massive processing parallelization.
    • NoSQL storage.
    • Sentiment analysis.
    • Machine Learning and Data Mining over large datasets.
  • Artificial Intelligence
    • Deep Learning.
    • Neural networks.
  • Semantics
    • Triplestores usage and customization, and their applicability in Linked Data and Big Data solutions.
    • Terminology servers and its application to semantic interoperability.
    • Reusing and engineering ontologies for multiple purposes and domain.
    • Natural Language Processing techniques in Spanish and English.
Current Research Topics and Findings: 
  • Automatic deployment of Big Data architectures, components and services.
  • Cross-domain data integration.
  • Deep learning and neural networks.
  • Stream processing and stream analytics.
  • Interpretation and analysis of unstructured textual resources using Natural Language Processing, Machine Learning and Data Mining techniques.
  • Usage of Linked Data open tools for data publishing and linking.

Projects

ACANTO

A CyberphysicAl social NeTwOrk using robot friends
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H2020

Using robots to increase the number of older adults who engage in a regular and sustained physical activity.

CoeGSS

Centre of Excellence for Global Systems Science
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H2020

Advanced decision-support in the face of global challenges. It brings together the power of HPC and some of the most promising thinking on global systems in order to improve decisions in business, politics and civil society.

LeanBigData

Ultra-Scalable and Ultra-Efficient Integrated and Visual Big Data Analytics
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FP7

LeanBigData targets at building an ultra-scalable and ultra-efficient integrated big data platform addressing important open issues in big data analytics

MLi

Towards a MultiLingual Data Services infrastructure
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FP7

Providing the foundations and roadmap of a scalable platform for the joint development/enhancement and hosting of (multi-)language datasets, processing tools and basic services.

PHEME

Computing Veracity Across Media, Languages, and Social Networks
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FP7

Combination of big data analytics with advanced linguistic and visual methods. The results will be suitable for direct application in medical information systems and digital journalism.

QROWD

Because Big Data Integration is Humanly Possible
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H2020

Methods to perform cross-sectoral streaming Big Data integration including geographic, transport, meteorological, cross domain and news data, while capitalizing on human feedback channels.

TOREADOR

TrustwOrthy model-awaRE Analytics Data platfORm
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H2020

TOREADOR aims at overcoming some major hurdles that until now have prevented many European companies from reaping the full benefits of Big Data Analytics.

VELaSSCo

Visualization for Extremely Large-Scale Scientific Computing
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FP7

Development of a new concept of integrated end-user visual analysis methods with advanced management and post-processing algorithms for engineering modelling applications, scalable for real-time petabyte level simulations.