Propozycja zgłoszona do Ideal-ist Partner Search i opatrzona Quality Label.

PROJECT OVERVIEW

Call Identifier: H2020-ICT-2015
Objective: ICT 16 – 2015: Big data – research
Funding Schemes: Research & Innovation Actions
Evaluation Scheme: one stage
Closure Date: 14/04/2015

PROJECT DESCRIPTION

Proposal Outline:

Nowadays, advancements in big data analytics offer cost-effective opportunities to improve decision-making in critical development areas such as health care, employment, economic productivity, crime fighting, security, natural disaster prevention, and resource management. Most of information saved in industry and academic are featured by following unstructured models. In fact, more than 80% of all potentially useful business information is unstructured data, such as: sensor readings, process traces, web tracking, and console logs. In this regard, retrieval and extraction of the information are essential tasks in current applications, and the large number and complexity of unstructured data opens up many new possibilities for the analyst. In this scenario, Semantic Web and ontologies are powerful tools to structure and manage information from which analysis algorithms would take advantage. However, the number of existing software packages that provides the experts with canonical algorithms for the big data management and analysis is still limited. In addition, algorithms in these frameworks are designed to work separately for specific tasks, and even for specific domains (e. g. PageRank), without giving cue to work co-ordinately to face new emerging and most challenging tasks.

The proposed action aims at developing a computational framework for the generation of algorithmic components and workflows, which support the generation of new processing blocks with emerging functionalities. The application of semantics to the algorithmic composition is an actual challenge nowadays, which will provide the proposed framework with composition meaning and consistent design. The motivation of this action is to develop a computational framework able of using semantics in two levels: algorithmic level, by using metadata from the framework design, and case of study level, by using metadata from the application domain, thus providing the whole framework with comprehensive and intelligent mechanism to construct Smart Data for further applications. The new (Smart) components are thought to consider common and new functionalities in Big Data such as: real-time cross streaming, dimensionality reduction, map-reduce framework and extensions, near linear time algorithm design, property testing, metric embedding, crowdsourcing, smart workflow design, as well as Business Intelligent systems connection.

Our research team has a wide experience in developing technologies and its application in real fields such as Systems Biology and Social Networks Analytics. In concrete, our research lines include: scalable reasoning on very large data sets, middleware based on ontologies, discovery of semantic correspondences among ontologies, metaheuristic algorithms, multi-objective optimization, and MapReduce algorithms for Big Data Analytics.

We are currently participating in the Bioledge EU project (FP7) in Protein Production, and we have participated in a number of national and regional projects in the application of data management, integration and analysis in Life Sciences. We are also starting a SME project, namely SME-E-COMPASS EU project (FP7), on Big Data Analytics on e-shop visitor behavior and credit card transactions for fraud detection.

Expexted impact of the action:

• 1.     The Government will possess the data necessary for the formation and implementation of big data analytics technologies. It will be able to offer modern and sophisticated framework algorithms to a European ecosystem of hundreds of companies in different domains of application. New generated data would be a potential source of validated information for the sake of the European Open Data documenting improvement.

• 2.     Benefits for companies will be two-fold. First, companies focused in emergent technologies will use the new resulting data-information, as well as the new generated software components, to couple them with their own packages as additional tools for further applications. Second, companies in other domains will translate the computational framework developed to be applied to different applications like discovering knowledge in social networks in social media systems.

The research community will be supported with advanced real-time and predictive data analytics technologies that will be used as base-line components for further innovations on large scale systems. New research lines will be then founded on thoroughly validated methods by means of rigorous scaling tests.

PARTNER PROFILE SOUGHT

Required skills and Expertise:

We look for companies able to provide this action with an interesting business case with big volume of data. One of these companies would coordinate the outlined action.

Description of work to be carried out by the partner(s) sought:

We search collaborations to design, develop, deploy, and commercialize the proposed framework of semantic algorithmic composition. In concrete

Type of partner(s) sought:

• Software Company for developing, deploying and commercializing the generated SDF tools in real scenarios.
• Company/research group for linking Business Intelligent tools with SFD tools.
• Company providing business case for the use of smart/big data.

PROPOSER INFORMATION
Organisation: University of Malaga
Department: Lenguajes y Ciencias de la Computacion
Type of Organisation: University
Country: Spain
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