Jens Kirchner
Karlsruhe Institute of Technology
5 Papers
29 Citations
Jens Kirchner is an academic researcher from Karlsruhe Institute of Technology. The author has contributed to research in topics: Service (business) & Service level objective. The author has an hindex of 3, co-authored 5 publications. Previous affiliations of Jens Kirchner include Karlsruhe University of Applied Sciences & Linnaeus University.
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Papers
Classification vs. Regression - Machine Learning Approaches for Service Recommendation Based on Measured Consumer Experiences
Jens Kirchner,Andreas Heberle,Welf Löwe +2 more
- 27 Jun 2015
TL;DR: The results of the analysis of two machine learning approaches to predict the best service within this selection problem are presented, with results based on data measured on real Web services as well as on simulated data.
16
Service Level Achievements -- Distributed Knowledge for Optimal Service Selection
Jesper Andersson,Andreas Heberle,Jens Kirchner,Welf Löwe +3 more
- 14 Sep 2011
TL;DR: This paper discusses service compositions in an open market scenario where an automated best-fit service selection and composition is based on Service Level Achievements instead and continuous monitoring updates the actual Service Level Achievement which can lead to dynamically changing compositions.
11
Appropriate machine learning methods for service recommendation based on measured consumer experiences within a service market
Jens Kirchner,Philipp Karg,Andreas Heberle,Welf Löwe +3 more
- 22 Mar 2015
TL;DR: In this paper, the actual experience of the performance of services at consumers' side is a desirable foundation for service selection, considering the knowledge of previous performance experiences from a consume consumer.
•Journal Article
Service Recommendation Using Machine Learning Methods Based on Measured Consumer Experiences Within a Service Market
TL;DR: Among functionally similar services, service consumers are interested in the consumption of the service that performs best towards their optimization preferences and the experienced performance of a service is a good indicator of this.
3
Evaluation of the Employment of Machine Learning Approaches and Strategies for Service Recommendation
Jens Kirchner,Jens Kirchner,Andreas Heberle,Welf Löwe +3 more
- 15 Sep 2015
TL;DR: This paper presents the results of the evaluation of two machine learning approaches in combination with several learning strategies to predict the best service within this selection problem, based on data measured on real Web services as well as on simulated data.