Now showing 1 - 9 of 9
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Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning

2022-07-22 , Hecker, Dirk , Voß, Angelika , Wrobel, Stefan

Machine learning and artificial intelligence have become crucial factors for the competitiveness of individual companies and entire economies. Yet their successful deployment requires access to a large volume of training data often not even available to the largest corporations. The rise of trustworthy federated digital ecosystems will significantly improve data availability for all participants and thus will allow a quantum leap for the widespread adoption of artificial intelligence at all scales of companies and in all sectors of the economy. In this chapter, we will explain how AI systems are built with data science and machine learning principles and describe how this leads to AI platforms. We will detail the principles of distributed learning which represents a perfect match with the principles of distributed data ecosystems and discuss how trust, as a central value proposition of modern ecosystems, carries over to creating trustworthy AI systems.

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Fraunhofer-Allianz Big Data

2018 , Wrobel, Stefan , Hecker, Dirk

Big Data ist branchenübergreifend ein Management-Thema und verspricht der Wirtschaft Vorsprung durch strukturiertes Wissen, mehr Effizienz und Wertschöpfung. In den Unternehmen gibt es einen hohen Bedarf an Big-Data- Kompetenzen, individuellen Geschäftsmodellen und technischen Lösungen. Fraunhofer unterstützt Unternehmen dabei, ihre Datenschätze zu identifizieren und zu heben. Experten der Fraunhofer-Allianz Big Data zeigen auf, wie Unternehmen von der intelligenten Anreicherung und Analyse ihrer Daten profitieren können.

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Information and knowledge management

2009 , Paaß, Gerhard , Schneider, Daniel , Wrobel, Stefan

In recognition of knowledge as a valuable resource, there is a whole spectrum of processes, methods and systems for the generation, identification, representation, distribution and communication of knowledge, which aim to provide targeted support to individuals, organisations and enterprises, particularly in solving knowledge-based tasks. This is known as information management and knowledge management (which we will handle jointly in this article). Making the right knowledge available to the right people at the right time is considered a crucial factor in ensuring the efficiency and competitiveness of modern enterprises.

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International Data Spaces. Reference architecture for the digitization of industries

2019 , Otto, Boris , Ten Hompel, Michael , Wrobel, Stefan

The International Data Space (IDS) offers an information technology architecture for safeguarding data sovereignty within the corporate ecosystem. It provides a virtual space for data where data remains with the data owner until it is needed by a trusted business partner. When the data is shared, terms of use can be linked to the data itself. Analysis of six use cases from the first phase of the prototype implementation of the IDS architecture shows that the focus lies on the standardized interface, the information model for describing data assets, and the connector component. Further use cases are planned for the next wave of implementation that are based on the broker functionality and require the use of vocabularies for simple data integration. In addition, companies need to standardize the principles that are translated into the terms of use. These principles need to be shaped, described, documented, and implemented in a simple and understandable way. They also need to be understood in the same way by different actors in the corporate ecosystem, thus requiring semantic standardization. Furthermore, the IDS Reference Architecture Model needs to be set in context with respect to related models. In the F3 use case, an OPC UA adapter is used. Additional use cases for integration with the Plattform Industrie 4.0 administration shell and Industrial Internet Reference Architecture are pending. The IDS Architecture is also increasingly being utilized in so-called verticalization initiatives, in healthcare and in the energy sector for example. These kinds of initiatives - like the Materials Data Space - demonstrate the crossdomain applicability of the architectural components and provide information about further development needs. Finally, in anticipation of the future development of the use cases and utilization of the IDS, work on the economic valuation of data and on the settlement and pricing of data transactions must be accelerated.

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Industrial Data Space : Referenzarchitektur für die Digitalisierung der Wirtschaft

2018 , Otto, Boris , Ten Hompel, Michael , Wrobel, Stefan

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Visual analytics methods for movement data

2007 , Andrienko, Gennady , Andrienko, Natalia , Kopanakis, I. , Litgenberg, A. , Wrobel, Stefan

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Fraunhofer Big Data and Artificial Intelligence Alliance

2019 , Wrobel, Stefan , Hecker, Dirk

Big data is a management issue across sectors and promises to deliver a competitive advantage via structured knowledge, increased efficiency and value creation. Within companies, there is significant demand for big data skills, individual business models, and technological solutions. Fraunhofer assists companies to identify and mine their valuable data. Experts from Fraunhofers Big Data and Artificial Intelligence Alliance demonstrate how companies can benefit from an intelligent enrichment and analysis of their data.

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Spatial data mining in practice

2010 , Körner, Christine , Hecker, Dirk , Krause-Traudes, Maike , May, Michael , Scheider, Simon , Schulz, Daniel , Stange, Hendrik , Wrobel, Stefan

Almost any data can be referenced in geographic space. Such data permit advanced analyses that utilize the position and relationships of objects in space as well as geographic background information. Even though spatial data mining is still a young research discipline, in the past years research advances have shown that the particular challenges of spatial data can be mastered and that the technology is ready for practical application when spatial aspects are treated as an integrated part of data mining and model building. In this chapter in particular, we give a detailed description of several customer projects that we have carried out and which all involve customized data mining solutions for business relevant tasks. The applications range from customer segmentation to the prediction of traffic frequencies and the analysis of GPS trajectories. They have been selected to demonstrate key challenges, to provide advanced solutions and to arouse further research questions.

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Kernel methods for graphs

2006 , Gärtner, Thomas , Horvath, Tamas , Le, Q.V. , Smola, A. , Wrobel, Stefan