Nachhaltige Wirkungen der Integrationsprogramme

Welche Auswirkungen haben die Integrationsprogramme der Sozialhilfe auf die Teilnehmenden im Hinblick auf ihre soziale oder berufliche Integration? Um die Wirkung messen zu können, wurden zwischen November 2015 und Februar 2017 drei Befragungen von Teilnehmenden von Integrationsprogrammen durchgeführt: bei Programmbeginn, bei Programmende nach sechs Monaten sowie neun Monate nach Programmende

Propelling the Potential of Enterprise Linked Data in Austria. Roadmap and Report

In times of digital transformation and considering the potential of the data-driven economy, it is crucial that data is not only made available, data sources can be trusted, but also data integrity can be guaranteed, necessary privacy and security mechanisms are in place, and data and access comply with policies and legislation. In many cases, complex and interdisciplinary questions cannot be answered by a single dataset and thus it is necessary to combine data from multiple disparate sources. However, because most data today is locked up in isolated silos, data cannot be used to its fullest potential. The core challenge for most organisations and enterprises in regards to data exchange and integration is to be able to combine data from internal and external data sources in a manner that supports both day to day operations and innovation. Linked Data is a promising data publishing and integration paradigm that builds upon standard web technologies. It supports the publishing of structured data in a semantically explicit and interlinked manner such that it can be easily connected, and consequently becomes more interoperable and useful. The PROPEL project - Propelling the Potential of Enterprise Linked Data in Austria - surveyed technological challenges, entrepreneurial opportunities, and open research questions on the use of Linked Data in a business context and developed a roadmap and a set of recommendations for policy makers, industry, and the research community. Shifting away from a predominantly academic perspective and an exclusive focus on open data, the project looked at Linked Data as an emerging disruptive technology that enables efficient enterprise data management in the rising data economy. Current market forces provide many opportunities, but also present several data and information management challenges. Given that Linked Data enables advanced analytics and decision-making, it is particularly suitable for addressing today's data and information management challenges. In our research, we identified a variety of highly promising use cases for Linked Data in an enterprise context. Examples of promising application domains include "customization and customer relationship management", "automatic and dynamic content production, adaption and display", "data search, information retrieval and knowledge discovery", as well as "data and information exchange and integration". The analysis also revealed broad potential across a large spectrum of industries whose structural and technological characteristics align well with Linked Data characteristics and principles: energy, retail, finance and insurance, government, health, transport and logistics, telecommunications, media, tourism, engineering, and research and development rank among the most promising industries for the adoption of Linked Data principles. In addition to approaching the subject from an industry perspective, we also examined the topics and trends emerging from the research community in the field of Linked Data and the Semantic Web. Although our analysis revolved around a vibrant and active community composed of academia and leading companies involved in semantic technologies, we found that industry needs and research discussions are somewhat misaligned. Whereas some foundation technologies such as knowledge representation and data creation/publishing/sharing, data management and system engineering are highly represented in scientific papers, specific topics such as recommendations, or cross-topics such as machine learning or privacy and security are marginally present. Topics such as big/large data and the internet of things are (still) on an upward trajectory in terms of attention. In contrast, topics that are very relevant for industry such as application oriented topics or those that relate to security, privacy and robustness are not attracting much attention. When it comes to standardisation efforts, we identified a clear need for a more in-depth analysis into the effectiveness of existing standards, the degree of coverage they provide with respect the foundations they belong to, and the suitability of alternative standards that do not fall under the core Semantic Web umbrella. Taking into consideration market forces, sector analysis of Linked Data potential, demand side analysis and the current technological status it is clear that Linked Data has a lot of potential for enterprises and can act as a key driver of technological, organizational, and economic change. However, in order to ensure a solid foundation for Enterprise Linked Data include there is a need for: greater awareness surrounding the potential of Linked Data in enterprises, lowering of entrance barriers via education and training, better alignment between industry demands and research activities, greater support for technology transfer from universities to companies. The PROPEL roadmap recommends concrete measures in order to propel the adoption of Linked Data in Austrian enterprises. These measures are structured around five fields of activities: "awareness and education", "technological innovation, research gaps, standardisation", "policy and legal", and "funding". Key short-term recommendations include the clustering of existing activities in order to raise visibility on an international level, the funding of key topics that are under represented by the community, and the setup of joint projects. In the medium term, we recommend the strengthening of existing academic and private education efforts via certification and to establish flagship projects that are based on national use cases that can serve as blueprints for transnational initiatives. This requires not only financial support, but also infrastructure support, such as data and services to build solutions on top. In the long term, we recommend cooperation with international funding schemes to establish and foster a European level agenda, and the setup of centres of excellence

Semantic stream processing of environmental data

Zusammenfassung in deutscher SpracheWhether we cope successfully or fail to deal with the world's environmental challenges will be determined in cities where, since 2008, more than half of the global population resides. Recently, also the application of computer science methods to solve environmental issues is increasingly promising. In this thesis we present an approach to enable citizens to make well-informed real time decisions based on environmental data. To this end, we leverage semantic web technologies as a practical means to overcome the obstacles of (i) environmental data integration, (ii) identifying data stream management engines to process real time environmental data, and (iii) enabling ecient use of environmental data streams for city stakeholders. We develop an ontology-based approach to integrate highly heterogeneous and dynamic environmental data sources. We present a novel vocabulary that combines and extends two de-facto standard vocabularies, that is, the Semantic Sensor Network Ontology and the RDF Data Cube Vocabulary. Further, we create a framework to evaluate suitable RDF Stream Processing (RSP) engines based on the special requirements of the environmental data domain, such as processing of high-frequency data, providing correct results, and scalability. This framework called YABench facilitates the identication of appropriate RSP engines under varying circumstances for scenarios in the environmental domain. After we identify C-SPARQL as a suitable RSP engine, we propose Linked Streaming Widgets. Linked Streaming Widgets represent lightweight semantic modules based on stream data, which can be combined to web applications by end users. By doing so, users can author their own mashups integrating environmental stream data sources, ultimately supporting well-informed decision making. We implement this concept as an extension of a mashup platform. To demonstrate its feasibility, we present and discuss two use cases based on citybike and air quality data, respectively, and perform performance evaluations indicating the practicability of Linked Streaming Widgets.17