SEE Unit - Projects

While the touristic service offers become present and bookable in abundance on the ICT communication channels, TourPack aims to build a linked data -empowered system for touristic service packaging. Integrating information from multiple sources and systems employing linked data as a global information integration platform, and mining from the depths of the “closed” data, the touristic service package production system will be able to cater to creating the most optimal travel experience for the traveler. Further, the service packages will be efficiently published and made bookable to the end consumers via intelligently selected most suitable communication and booking channels: especially the ICT channels with rapidly growing user audiences, such as the social media and the mobile apps.

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.

Healthcare is nowadays facing a huge number of patients worldwide mainly due to the growing aging population and population with chronic non-communicable diseases in western countries. This increase in patients implies (among others) an increase of the techniques and medical knowledge for diagnosis treatment that are needed which turns into a serious increase of the health care expenses. Hence, in order to stay financially feasible, there is a need to keep costs low while at the same time, level of quality in patient’s care should be guaranteed. Information systems and specifically, the EHR (electronic health record) play an important role to achieve such an ambitious goal. The EHR allows closing the gap between institution-specific patient data and collecting patient’s health data which can be also exchanged between various healthcare providers. Support interoperability between EHR systems is then an important challenge and different standards have already been developed to address it. However, one problem that has not been tackled sufficiently is the functionality level of the EHR. EHR systems are still monolithic systems from the functionality perspective. Hence, based on service orientation, OntoHealth aims to establish functionally flexible, standard-based EHR systems that allow answering respectively support solving clinical problems on an individual, case-specific basis.

While the mass consumers' demand and expectations in the energy efficiency field grow, the providers and manufacturers of electrical appliances are searching for the approaches and infrastructures enabling them to build new kind of added-value services, based on the large volumes of data available from the appliances. Thus, the goal of the project OpenFridge is to design and develop a pilot simple and scalable Internet of Things data infrastructure, empowering building new services based on the typical home appliance data, e.g. data on the energy consumption of the fridge. The infrastructure would be comprised of semantic domain models for opening of the appliances data, data analytics module for aggregation of the raw Internet of Things energy data in adding value energy efficiency information, as well as provisioning of this information to interested stakeholders (appliance manufacturers, end users, utilities, municipalities, etc.) under the new access mechanisms and business models. Further, the pilot service prototypes will showcase the OpenFridge approach and infrastructure, and involve the real end user communities with varying sociodemographic factors and gender characteristics.

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.

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

• the increasing reliance of business on large public data;
• 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
• 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.