Projects

Big Data is an emerging field where innovative technology offers alternatives to resolve the inherent problems that appear when working with huge amounts of data, providing new ways to reuse and extract value from information. Three main dimensions characterize Big Data: huge variety of data format, often time-sensitive and large. Big Data offers tremendous untapped potential value for many sectors but no specific intelligent-large-data-handling/brokering industrial sector exists. Furthermore, from an industrial adoption point of view, Europe is lagging behind US in Big Data technologies. A clear strategy to align supply and demand is needed as a way of increasing competitiveness of European industries. Building an industrial community around Big Data in Europe will be the priority of this project, together with setting up the necessary collaboration and dissemination infrastructure to link technology suppliers, integrators and leading user organizations. Big Data Public Private Forum (BIG) will work towards the definition and implementation of a clear strategy that tackles the necessary efforts in terms of research and innovation, but it will also provide a major boost for technology adoption and supporting actions from the European Commission in the successful implementation of the Big Data economy.

Managing risk situations is a topic of interest for most companies ranging from small and medium enterprises to large entities. In broader terms, risk management is understood as the identification, assessment, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability and/or impact of unfortunate events or to maximize the realization of opportunities . Risk management can help companies manage important adverse future events and help establishing procedures and means to measure, report, mitigate and manage such risks. However, as the sources of information from where risks and threats can be detected is exponential growing, both in size and heterogeneity, it becomes nearly impossible for companies to deal with the challenging task. Relevant sources that one can include but are not limited to news sites (e.g. Reuters, CNN), social media channels (e.g. Facebook, Twitter), travel plan documents, emails, etc. Moreover, information available in these sources must be interpreted and correlated with other information sources. Nowadays the overall process requires the strict attention and intervention of the human factor, which obviously leads to higher costs and not always to accurate results. While large companies can invest large resources in performing such tasks, small and medium enterprises have limited resources and are facing the situation of ignoring the risk management problem which often leads to considerable loses. ESA will be an intelligent system which offers a semi-automatic solution that enables enterprises to conduct proper risk assessment by processing large amount of live data sources, to effectively and efficiently analyze and derive proper actions for risk management through automatic reasoning on the data collected from such sources and finally to provide services and applications for its employees and other connected parties in a cost effective manner.

The e-Freight project is aimed at supporting, from a transport perspective, the three pillars of European Policy namely:

• Strengthening of the internal market and competitiveness;
• Improving regulation to create a more dynamic business environment;
• Promoting sustainable development.

Specifically e-Freight will contribute to the goals of the Freight transport Logistics Action Plan (Oct 2007), and ITS Action Plan (Oct 2008) pertaining to the development of:

1. A standard framework for freight information exchange covering all transport modes and all stakeholders.
2. A European Single Transport Document for carriage of goods with all the necessary legislative support,
   irrespective of mode.
3. A Single Window (single access point) for administrative procedures in all modes.
4. Simple, harmonised border crossings procedures for all modes of transport for all EU member states.
5. Simple procedures and the necessary infrastructure for establishing secure and efficient transport corridors between Europe, USA, and Asia.

Non-relational data management is emerging as a critical need for the new data economy based on large, distributed, heterogeneous, and complexly structured data sets. This new data management paradigm also provides an opportunity for research results to impact young innovative companies working on new RDF and graph data management technologies to start playing a significant role in this new data economy. Standards and benchmarking are two of the most important factors for the development of new information technology, yet there is still no comprehensive suite of benchmarks and benchmarking practices for RDF and graph databases, nor is there an authority for setting benchmark definitions and auditing official results. Without them, the future development and uptake of these technologies is at risk by not providing industry with clear, user-driven targets for performance and functionality.The goal of the Linked Data Benchmark Council (LDBC) project is to create the first comprehensive suite of open, fair and vendor-neutral benchmarks for RDF/graph databases together with the LDBC foundation which will define processes for obtaining, auditing and publishing results. The core scientific innovation of LDBC is therefore to define meaningful benchmarks derived from a combination of actual usage scenarios combined with the technical insight of top database systems researchers and architects in the choke points of current technology. LDBC will bring together a broad community of researchers and RDF and graph database vendors to establish an independent authority, the LDBC foundation, responsible for specifying benchmarks, benchmarking procedures and verifying/publishing results. The forum created will become a long-surviving, industry supported association similar to the TPC. Vendors and user organisations will participate in order to influence benchmark design and to make use of the obvious marketing opportunities.

By 2015, novel service-oriented management methodologies and the Future Internet universal business infrastructure will enable European virtual factories and enterprises to self-organize in distributed, autonomous, interoperable, non-hierarchical innovation ecosystems of tangible and intangible manufacturing assets, to be virtually described, on-the-fly composed and dynamically delivered as a Service, end-to-end along the globalised value chain." The first Grand Challenge for MSEE project is to make SSME (Service Science Management and Engineering) evolve towards Manufacturing Systems and Factories of the Future, i.e. from a methodological viewpoint to adapt, modify, extend SSME concepts so that they could be applicable to traditionally product-oriented enterprises; from an implementation viewpoint to instantiate Future Internet service oriented architectures and platforms for global manufacturing service systems. The second Grand Challenge for MSEE project is to transform current manufacturing hierarchical supply chains into manufacturing open ecosystems, i.e. on the one side to define and implement business processes and policies to support collaborative innovation in a secure industrial environment; on the other side to define a new collaborative architecture for ESA (Enterprise Software and Applications), to support business-IT interaction and distributed decision making in virtual factories and enterprises. The synthesis of the two Grand Challenges above in industrial business scenarios and their full adoption in some European test cases will result in new Virtual Factory Industrial Models, where service orientation and collaborative innovation will support a new renaissance of Europe in the global manufacturing context.

The main aim of PlanetData is to establish an interdisciplinary, sustainable European community of researchers, helping organizations to expose their data on the Web in a useful way. PlanetData will push forward the state-of-the-art in large-scale data management and its application to the creation of useful, open data sets. This is motivated by

1. the increasing reliance of business on large public data;
2. the uptake of open data principles in many vertical sectors, most notably eGovernment, for public or social good, to increase the efficiency of end-user services and enable novel business models; and
3. the need of research communities to make sense out of petabytes of scientific data, to describe and expose this data in ways that encourage and enable collaboration. Research questions relevant to large-scale data management are inherently interdisciplinary; their durable resolution requires the building of bridges between the different research communities that currently exist in isolation from each other.

PRELIDA will target the particular stakeholders of the Linked Data community, including data providers, service providers, technology providers and end user communities. These stakeholders have not been traditionally targeted by the Digital Preservation community, and are typically not aware of the digital preservation solutions already available. So an important task of PRELIDA is to raise awareness of existing preservation solutions and to facilitate their uptake.

At the same time, the Linked Data cloud has specific characteristics in terms of structuring, interlinkage, dynamicity and distribution, that pose new challenges to the preservation community.

PRELIDA will organise in-depth discussions among the two communities to identify which of these characteristics require novel solutions, and to develop road maps for addressing the new challenges.

The RENDER project addresses the challenges arising when analyzing the dynamics of online information. An amazing diversity of opinions, viewpoints, mind sets and backgrounds can be extracted from Web content. Large scale data management calls upon fundamental challenges with respect to the purposeful access, processing and management of the Web’s dynamic information avalanche, in particular when leveraging the diversity that inherently unfolding through world wide scale collaboration.

RENDER will engage with these challenges by developing methods, techniques, software and data sets that will leverage diversity as a crucial source of innovation and creativity, whilst providing enhanced support for feasibly managing data at very large scale, and for designing novel algorithms that reflect diversity in the ways information is selected, ranked, aggregated, presented and used in popular communication and collaboration platforms such as MediaWiki, Twitter, WordPress and Google Wave.

RENDER will help to realize a world where information is acquired and shared in a fundamentally different manner than the consensual approach promoted by movements such as Web 2.0, and where communication and collaboration across the borders of social, cultural or professional communities are truly enabled via advanced Web technology, supporting one of the credos of European society: “United in diversity”.

The project will run for 36 months starting in October 2010.

RESIDE is an ÖAD co-funded mobility project between Austria and China. The project aims to enable development and deployment of highly personalized services based on sensor, smart meter and user input data, applicable to the new scenarios emerging from the renewable energy introduction and the settings of both countries. RESIDE objectives are:

1. To build an energy efficiency and passive house data infrastructure, identifying common points of interest in relevant data usage, based on the renewable service specification, smart home sensor and metering data repositories that are available to the consortium, with a semantic access and business models ensuring the sustainability of the infrastructure maintenance and use.

2. Build renewable energy related pilot services on this infrastructure that can be eventually durably exploited with the use for both countries, as well as enable and motivate third-party developers to build added value services on this repository and benefit from them.

SUPPORT aims to raise the current level of port security by integrating legacy port systems with new surveillance and information management systems. SUPPORT will provide the necessary and sufficient security level to satisfy evolving international regulations and standards while efficiently supporting the complexity of the real port environment. As part of this, SUPPORT will also facilitate efficient and, where required, real-time exchange of security related information within the supply chain and between ports and authorities. SUPPORT partners include a number of ports that have been selected to represent typical, but different operations. Starting from the perspective of the partner port operations, the project will use a cost-benefit method to identify the main security gaps and will describe security measures to maintain or augment the efficient and secure operation of these ports. The approach will combine creative and analytical techniques to identify as many relevant threats as possible. After making an inventory of security gaps these will be developed into generic port security models. The models will be used to suggest security upgrade solutions, taking into account the cost-benefit factors of the available technology. Peer-to-peer communication and decision support tools incorporating semantic technologies will be developed, using as far as possible standard open architecture software, accessible to all the port security stakeholders. The results will be demonstrated at the ports of Gothenburg and Piraeus. SUPPORT will include policy and standardisation proposals and training for participating port personnel as well as dissemination activities for other ports and stakeholders. One aim of SUPPORT is to provide general methods and technology supported by training services that can be used by any European port to efficiently enhance its security level.