Now showing 1 - 3 of 3
  • Publication
    Metadata only
    Toward cognitive pipelines of medical assistance algorithms
    (Springer, 2016-09-01)
    Philipp, Patrick
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    Katic, Darko
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    Weber, Christian
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    Götz, Michael
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    Rettinger, Achim
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    Speidel, Stefanie
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    Kämpgen, Benedikt
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    Nolden, Marco
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    Wekerle, Anna Laura
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    Dillmann, Rüdiger
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    Kenngott, Hannes
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    Müller, Beat
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    Studer, Rudi
    Purpose: Assistance algorithms for medical tasks have great potential to support physicians with their daily work. However, medicine is also one of the most demanding domains for computer-based support systems, since medical assistance tasks are complex and the practical experience of the physician is crucial. Recent developments in the area of cognitive computing appear to be well suited to tackle medicine as an application domain. Methods: We propose a system based on the idea of cognitive computing and consisting of auto-configurable medical assistance algorithms and their self-adapting combination. The system enables automatic execution of new algorithms, given they are made available as Medical Cognitive Apps and are registered in a central semantic repository. Learning components can be added to the system to optimize the results in the cases when numerous Medical Cognitive Apps are available for the same task. Our prototypical implementation is applied to the areas of surgical phase recognition based on sensor data and image progressing for tumor progression mappings. Results: Our results suggest that such assistance algorithms can be automatically configured in execution pipelines, candidate results can be automatically scored and combined, and the system can learn from experience. Furthermore, our evaluation shows that the Medical Cognitive Apps are providing the correct results as they did for local execution and run in a reasonable amount of time. Conclusion: The proposed solution is applicable to a variety of medical use cases and effectively supports the automated and self-adaptive configuration of cognitive pipelines based on medical interpretation algorithms.
  • Publication
    Metadata only
    Knowledge discovery meets linked APIs
    (RWTH, 2013)
    Hoxha, Julia
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    Korevaar, Peter
    Knowledge Discovery and Data Mining (KDD) is a very wellestablished research field with useful techniques that explore patterns and regularities in large relational, structured and unstructured datasets. Theoretical and practical development in this field have led to useful and scalable solutions for the tasks of pattern mining, clustering, graph mining, and predictions. In this paper, we demonstrate that these approaches represent great potential to solve a series of problems and make further optimizations in the setting of Web APIs, which have been significantly increasing recently. In particular, approaches integrating Web APIs and Linked Data, also referred to as Linked APIs, provide novel opportunities for the application of synergy approaches with KDD methods. We give insights on several aspects that can be covered through such synergy approach, then focus, specifically, on the problem of API usage mining via statistical relational learning.We propose a Hidden Relational Model, which explores the usage of Web APIs to enable analysis and prediction. The benefit of such model lies on its ability to capture the relational structure of API requests. This approach might help not only to gain insights about the usage of the APIs, but most importantly to make active predictions on which APIs to link together for creating useful mashups, or facilitating API composition.
  • Publication
    Metadata only
    Telecommunication mashups using RESTful services
    (Springer, 2010-12-01)
    Duke, Alistair
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    Stincic, Sandra
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    Davies, John
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    Álvaro Rey, Guillermo
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    Pedrinaci, Carlos
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    Domingue, John
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    Liu, Dong
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    Lecue, Freddy
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    Mehandjiev, Nikolay
    Evolution in the telecommunications sector has led to companies within it providing APIs for their products and services, allowing others to build communication services into their own service offerings. In order to support mass adoption of this new approach, consumers of these APIs (many of which are RESTful) must be supported by a reduction in the complexity involved with describing, finding, composing and invoking them. Existing efforts to provide automation have, in general, focused on WSDL services rather than REST services. The paper explores the approach of the SOA4All project in supporting interaction with REST services which is being applied in a telecommunications focused case study. © 2010 Springer-Verlag.