The goal of Adaptive Services Grid (ASG) is to develop a proof-of-concept prototype of an open development platform for adaptive services discovery, creation, composition, and enactment. ASG provides the integration of its sub-projects in the context of an open platform, including tool development by small and medium sized enterprises. Based on semantic specifications of requested services by service customers, ASG discovers appropriate services, composes complex processes and - if required - generates software to create new application services on demand. Subsequently, application services will be provided through the underlying computational grid infrastructure based on adaptive process enactment technology. In the proposed project, methods and concepts from software architectures, software development methodologies, Web services composition and workflow process planning and coordination will be complemented by recent results in domain engineering, software generation and Semantic web and agent negotiation research. To achieve its goal, ASG addresses scientific and technological issues making use of the knowledge and expertise of major European research institutions with significant contributions from the software, telecommunications, and telematics industry.

Scientific Workflows are very important to support life-science researcher in the discovery of cures for many diseases. Life-science researchers need easy to use and as much as possible automated software frameworks, so as to require for them a minimum knowledge of software technologies to accomplish their daily research task without extra learning efforts and reducing their manual efforts. Research in scientific workflows produced many results in this area, but still a huge manual effort is required to scientists to integrate different available data and compose different available services. Semantic Web is currently one of the most interesting research areas with focus on automation of interoperability composition, and discovery of services that can bring a huge contribute to the research in scientific workflow. The objective of the collaboration among the University of Innsbruck and the University of Malaga, is to bring together their respective expertises in Semantic Web technologies and scientific workflows to produce a prototype of a innovative Semantic based framework for scientific workflows.

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.

The Big data roadmap and cross-disciplinarY community for addressing socieTal Externalities (BYTE) project will assist European science and industry in capturing the positive externalities and diminishing the negative externalities associated with big data in order to gain a greater share of the big data market by 2020.

To keep pace with market demand for more performance and functionality in today’s mobile phones, digital cameras, computers, automotive systems and other electronics products, manufacturers pack billions of transistors onto a single chip. This massive integration parallels the shift to ever-smaller process geometries, where the chip’s transistors and other physical features can be smaller than the wavelength of light used to print them. Designing and manufacturing semiconductor devices with such phenomenal scale, complexity and technological challenges would not be possible without electronic design automation (EDA). It is essential for everything from verifying that the myriad transistors do what the designer intended to dealing with physical effects on electrons traveling miles of wires with widths sometimes measuring less than 100 nanometers. Cadence Design Systems is the world's leading EDA company. Cadence customers use our software, hardware, and services to overcome a range of technical and economic hurdles. DERI Innsbruck's Business Development provided consultancy service to Cadence in 2007.

“By 2020 enterprise collaboration and interoperability services will become an invisible, pervasive and self-adaptive knowledge and business utility at disposal of the European networked enterprises from any industrial sector and domain in order to rapidly set-up, efficiently manage and effectively operate different forms of business collaborations, from the most traditional supply chains to the most advanced and dynamic business ecosystems.” The mission of the COIN IP is to study, design, develop and prototype an open, self-adaptive, generic ICT integrated solution to support the above 2020 vision. COIN business-pervasive open-source service platform will be able to expose, integrate, compose and mash-up in a secure and adaptive way existing and innovative to be developed Enterprise Interoperability and Enterprise Collaboration services, by applying intelligent maturity models, business rules and self-adaptive decision-support guidelines to guarantee the best combination of the needed services in dependence of the business context, as industrial sector and domain, size of the companies involved, openness and dynamics of collaboration. This way, the Information Technology vision of Software as a Service (SaaS) will find its implementation in the field of interoperability among collaborative enterprises, supporting the various collaborative business forms, from supply chains to business ecosystems, and becoming for them like a utility, a commodity, the so-called Interoperability Service Utility (ISU).
The COIN project will finally develop an original business model based on the SaaS-U (Software as a Service-Utility) paradigm where the open-source COIN service platform will be able to integrate both free-of-charge and chargeable, open and proprietary services depending on the case and business policies.

The objectives of COG are: Demonstrate the Applicability of Grid Technologies to Industry Realize the Concept of an Information Grid Incorporating Real Corporate Data Give Semantics to Corporate Data Formats, including Legacy, Relational, and XML, through a Detailed Mapping to a Central Ontology Demonstrate the Technological Innovation of Automatically Translating Data Between Data Formats on the Grid by Way of a Semantic Mapping to a Central Ontology Publish a Reusable Ontology with Concepts from Discrete Manufacturing, and Specifically Automotive Capture Best Practices in Achieving these Objectives and Document the Methodology Disseminate the Methodology and the Results to Europe's Corporations Re-use of Methodology in Industry

The creation of derivative data works, i.e. for purposes like content creation, service delivery or process automation, is often accompanied by legal uncertainty about usage rights and high costs in the clearance of licensing issues. The DALICC project aims to develop a software framework that supports the automated clearance of rights issues in the creation of derivative data works. In essence, DALICC helps to determine which information can be shared with whom to what extent under which conditions, thus lowering the costs of rights clearance and stimulating the data economy.