@inproceedings{319, abstract = {{Services are self-contained and platform independent software components that aim at maximizing software reuse. The automated composition of services to a target software artifact has been tackled with many AI techniques, but existing approaches make unreasonably strong assumptions such as a predefined data flow, are limited to tiny problem sizes, ignore non-functional properties, or assume offline service repositories. This paper presents an algorithm that automatically composes services without making such assumptions. We employ a backward search algorithm that starts from an empty composition and prepends service calls to already discovered candidates until a solution is found. Available services are determined during the search process. We implemented our algorithm, performed an experimental evaluation, and compared it to other approaches.}}, author = {{Mohr, Felix and Jungmann, Alexander and Kleine Büning, Hans}}, booktitle = {{Proceedings of the 12th IEEE International Conference on Services Computing (SCC)}}, pages = {{57----64}}, title = {{{Automated Online Service Composition}}}, doi = {{10.1109/SCC.2015.18}}, year = {{2015}}, } @inproceedings{272, abstract = {{Automated service composition aims at automatically generating software solutions based on services to provide more complex functionality. In this paper, we give an initial overview about why adaptivity becomes increasingly important when aiming for automated composition of service functionality in dynamic and freely accessible environments such as service markets. We systematically derive dependencies among crucial processes such as service composition and service execution in a holistic view. Furthermore, we briefly discuss the influences and effects of changes in the environment according to the derived dependencies, and derive possible future research directions. }}, author = {{Jungmann, Alexander}}, booktitle = {{Proceedings of the IEEE 11th World Congress on Services (SERVICES)}}, pages = {{329----332}}, title = {{{On Adaptivity for Automated Composition of Service Functionality}}}, doi = {{10.1109/SERVICES.2015.57}}, year = {{2015}}, } @inproceedings{345, abstract = {{Automatically composing service-based software solutions is a challenging task. Considering context information during this service composition process is even more challenging. In domains such as image processing, however, context-sensitivity is inherent and cannot be ignored when developing techniques for automatic service composition. Formal approaches tend to create ambiguous solutions, whenever the expressive power of the applied formalism is limited. For example, services may have the same formal specification, although their actual functionality depends on the concrete context. In order to satisfy individual user requests while providing data-dependent functionality, formal approaches have to be extended. We propose to incorporate Reinforcement Learning techniques and combine them with planning based composition approaches. While planning ensures formally correct solutions, learning enables the composition process to resolve ambiguity by implicitly considering context information. Preliminary results show that our combined approach adapts to a static context while still satisfying formally specified requirements.}}, author = {{Jungmann, Alexander and Kleinjohann, Bernd}}, booktitle = {{Proceedings of the 6th International Conference on Cloud Computing Technology and Science (CloudCom)}}, pages = {{755--758}}, title = {{{Towards Context-Sensitive Service Composition for Service-Oriented Image Processing}}}, doi = {{10.1109/CloudCom.2014.154}}, year = {{2014}}, } @inproceedings{346, abstract = {{One future goal of service-oriented computing is to realize global markets of composed services. On such markets, service providers offer services that can be flexibly combined with each other. However, most often, market participants are not able to individually estimate the quality of traded services in advance. As a consequence, even potentially profitable transactions between customers and providers might not take place. In the worst case, this can induce a market failure. To overcome this problem, we propose the incorporation of reputation information as an indicator for expected service quality. We address On-The-Fly Computing as a representative environment of markets of composed services. In this environment, customers provide feedback on transactions. We present a conceptual design of a reputation system which collects and processes user feedback, and provides it to participants in the market. Our contribution includes the identification of requirements for such a reputation system from a technical and an economic perspective. Based on these requirements, we propose a flexible solution that facilitates the incorporation of reputation information into markets of composed services while simultaneously preserving privacy of customers who provide feedback. The requirements we formulate in this paper have just been partially met in literature. An integrated approach, however, has not been addressed yet.}}, author = {{Brangewitz, Sonja and Jungmann, Alexander and Petrlic, Ronald and Platenius, Marie Christin}}, booktitle = {{Proceedings of the 6th International Conferences on Advanced Service Computing (SERVICE COMPUTATION)}}, pages = {{49--57}}, title = {{{Towards a Flexible and Privacy-Preserving Reputation System for Markets of Composed Services}}}, year = {{2014}}, } @inproceedings{353, abstract = {{There are many technologies for the automation of processesthat deal with services; examples are service discovery and composition.Automation of these processes requires that the services are described semantically. However, semantically described services are currently not oronly rarely available, which limits the applicability of discovery and composition approaches. The systematic support for creating new semanticservices usable by automated technologies is an open problem.We tackle this problem with a template based approach: Domain independent templates are instantiated with domain specific services andboolean expressions. The obtained services have semantic descriptionswhose correctness directly follows from the correctness of the template.Besides the theory, we present experimental results for a service repository in which 85% of the services were generated automatically.}}, author = {{Mohr, Felix and Walther, Sven}}, booktitle = {{Proceedings of the 14th International Conference on Software Reuse (ICSR)}}, pages = {{188--203}}, title = {{{Template-based Generation of Semantic Services}}}, doi = {{10.1007/978-3-319-14130-5_14}}, year = {{2014}}, } @inproceedings{366, abstract = {{On-The-Fly (OTF) Computing constitutes an approach towards highly dynamic and individualized software markets. Based on service-oriented computing, OTF Computing is about realizing global markets of services that can be flexibly combined. We report on our current research activities, the security and privacy implications thereof, and our approaches to tackle the challenges. Furthermore, we discuss how the security and privacy challenges are addressed in research projects similar to OTF Computing.}}, author = {{Petrlic, Ronald and Jungmann, Alexander and Platenius, Marie Christin and Schäfer, Wilhelm and Sorge, Christoph}}, booktitle = {{Tagungsband der 4. Konferenz Software-Technologien und -Prozesse (STeP 2014)}}, pages = {{131--142}}, title = {{{Security and Privacy Challenges in On-The-Fly Computing}}}, year = {{2014}}, } @inproceedings{447, abstract = {{Automatic service composition is still a challengingtask. It is even more challenging when dealing witha dynamic market of services for end users. New servicesmay enter the market while other services are completelyremoved. Furthermore, end users are typically no experts in thedomain in which they formulate a request. As a consequence,ambiguous user requests will inevitably emerge and have tobe taken into account. To meet these challenges, we proposea new approach that combines automatic service compositionwith adaptive service recommendation. A best first backwardsearch algorithm produces solutions that are functional correctwith respect to user requests. An adaptive recommendationsystem supports the search algorithm in decision-making.Reinforcement Learning techniques enable the system to adjustits recommendation strategy over time based on user ratings.The integrated approach is described on a conceptional leveland demonstrated by means of an illustrative example fromthe image processing domain.}}, author = {{Jungmann, Alexander and Mohr, Felix and Kleinjohann, Bernd}}, booktitle = {{Proceedings of the 10th World Congress on Services (SERVICES)}}, pages = {{346--353}}, title = {{{Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services}}}, doi = {{10.1109/SERVICES.2014.68}}, year = {{2014}}, } @misc{454, author = {{Heldt, Waleri}}, publisher = {{Universität Paderborn}}, title = {{{Automated Service Composition: Adaption of the ASTRO Approach}}}, year = {{2014}}, } @inproceedings{457, abstract = {{Automatically composing service-based software solutionsis still a challenging task. Functional as well as nonfunctionalproperties have to be considered in order to satisfyindividual user requests. Regarding non-functional properties,the composition process can be modeled as optimization problemand solved accordingly. Functional properties, in turn, can bedescribed by means of a formal specification language. Statespacebased planning approaches can then be applied to solvethe underlying composition problem. However, depending on theexpressiveness of the applied formalism and the completenessof the functional descriptions, formally equivalent services maystill differ with respect to their implemented functionality. As aconsequence, the most appropriate solution for a desired functionalitycan hardly be determined without considering additionalinformation. In this paper, we demonstrate how to overcome thislack of information by means of Reinforcement Learning. Inorder to resolve ambiguity, we expand state-space based servicecomposition by a recommendation mechanism that supportsdecision-making beyond formal specifications. The recommendationmechanism adjusts its recommendation strategy basedon feedback from previous composition runs. Image processingserves as case study. Experimental results show the benefit of ourproposed solution.}}, author = {{Jungmann, Alexander and Mohr, Felix and Kleinjohann, Bernd }}, booktitle = {{Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA)}}, pages = {{105--112}}, title = {{{Applying Reinforcement Learning for Resolving Ambiguity in Service Composition}}}, doi = {{10.1109/SOCA.2014.48}}, year = {{2014}}, } @inproceedings{407, abstract = {{Automated programming aims at automatically assembling a new software artifact from existing software modules. Although automated programming was revitalized through automated software composition in the last decade, the problem cannot be considered solved. Automated software composition is widely accepted as being a planning task, but the problem is that it has very special properties that other planning problems do not have and that are commonly overseen. These properties usually imply that the composition problem cannot be solved with standard planning tools. This paper gives a brief and intuitive description of the planning problem that most approaches are based on. It points out special properties of this problem and explains why it is not adequate to solve the problem with classical planning tools as done by most existing approaches.}}, author = {{Mohr, Felix}}, booktitle = {{Proceedings of the 29th International Conference on Automated Software Engineering (ASE)}}, pages = {{ 895----898}}, title = {{{Issues of Automated Software Composition in AI Planning}}}, doi = {{10.1145/2642937.2653470}}, year = {{2014}}, } @article{410, abstract = {{One goal of service-oriented computing is to realize future markets of composed services. In such markets, service providers offer services that can be flexibly combined with each other. However, although crucial for decision-making, market participants are usually not able to individually estimate the quality of traded services in advance. To overcome this problem, we present a conceptual design for a reputation system that collects and processes user feedback on transactions, and provides this information as a signal for quality to participants in the market. Based on our proposed concept, we describe the incorporation of reputation information into distinct decision-making processes that are crucial in such service markets. In this context, we present a fuzzy service matching approach that takes reputation information into account. Furthermore, we introduce an adaptive service composition approach, and investigate the impact of exchanging immediate user feedback by reputation information. Last but not least, we describe the importance of reputation information for economic decisions of different market participants. The overall output of this paper is a comprehensive view on managing and exploiting reputation information in markets of composed services using the example of On-The-Fly Computing.}}, author = {{Jungmann, Alexander and Brangewitz, Sonja and Petrlic, Ronald and Platenius, Marie Christin}}, journal = {{International Journal On Advances in Intelligent Systems (IntSys)}}, number = {{3&4}}, pages = {{572----594}}, publisher = {{IARIA}}, title = {{{Incorporating Reputation Information into Decision-Making Processes in Markets of Composed Services}}}, volume = {{7}}, year = {{2014}}, } @misc{424, author = {{Finkensiep, Christoph}}, publisher = {{Universität Paderborn}}, title = {{{Fast and Flexible Automatic Composition of Semantic Web Services}}}, year = {{2014}}, } @inproceedings{425, abstract = {{In this paper, we evaluate the robustness of our color-based segmentation approach in combination with different color spaces, namely RGB, L*a*b*, HSV, and log-chromaticity (LCCS). For this purpose, we describe our deterministic segmentation algorithm including its gradually transformation of pixel-precise image data into a less error-prone and therefore more robust statistical representation in terms of moments. To investigate the robustness of a specific segmentation setting, we introduce our evaluation framework that directly works on the statistical representation. It is based on two different types of robustness measures, namely relative and absolute robustness. While relative robustness measures stability of segmentation results over time, absolute robustness measures stability regarding varying illumination by comparing results with ground truth data. The significance of these robustness measures is shown by evaluating our segmentation approach with different color spaces. For the evaluation process, an artificial scene was chosen as representative for application scenarios based on artificial landmarks.}}, author = {{Jungmann, Alexander and Jatzkowski, Jan and Kleinjohann, Bernd}}, booktitle = {{Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP)}}, pages = {{648--655}}, title = {{{Evaluation of Color Spaces for Robust Image Segmentation}}}, year = {{2014}}, } @inproceedings{428, abstract = {{Services are self-contained software components that can be used platform independent and that aim at maximizing software reuse. A basic concern in service oriented architectures is to measure the reusability of services. One of the most important qualities is the functional reusability, which indicates how relevant the task is that a service solves. Current metrics for functional reusability of software, however, either require source code analysis or have very little explanatory power. This paper gives a formally described vision statement for the estimation of functional reusability of services and sketches an exemplary reusability metric that is based on the service descriptions.}}, author = {{Mohr, Felix}}, booktitle = {{Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC)}}, pages = {{411--418}}, title = {{{Estimating Functional Reusability of Services}}}, year = {{2014}}, } @misc{482, author = {{Bieshaar, Maarten}}, publisher = {{Universität Paderborn}}, title = {{{Statistisches Planen von Aktionen für autonome mobile Roboter in realen Umgebungen}}}, year = {{2013}}, } @inproceedings{485, abstract = {{Software composition has been studied as a subject of state based planning for decades. Existing composition approaches that are efficient enough to be used in practice are limited to sequential arrangements of software components. This restriction dramatically reduces the number of composition problems that can be solved. However, there are many composition problems that could be solved by existing approaches if they had a possibility to combine components in very simple non-sequential ways. To this end, we present an approach that arranges not only basic components but also composite components. Composite components enhance the structure of the composition by conditional control flows. Through algorithms that are written by experts, composite components are automatically generated before the composition process starts. Therefore, our approach is not a substitute for existing composition algorithms but complements them with a preprocessing step. We verified the validity of our approach through implementation of the presented algorithms.}}, author = {{Mohr, Felix and Kleine Büning, Hans}}, booktitle = {{Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS)}}, pages = {{676--680}}, title = {{{Semi-Automated Software Composition Through Generated Components}}}, doi = {{10.1145/2539150.2539235}}, year = {{2013}}, } @inproceedings{495, abstract = {{Automated service composition has been studied as a subject of state based planning for a decade. A great deal of service composition tasks can only be solved if concrete output values of the services are considered in the composition process. However, the fact that those values are not known before runtime leads to nondeterministic planning problems, which have proven to be notoriously difficult in practical automated service composition applications. Even though this problem is frequently recognized, it has still received remarkably few attention and remains unsolved.This paper shows how nondeterminism in automated service composition can be reduced. We introduce context rules as a means to derive semantic knowledge from output values of services. These rules enable us to replace nondeterministic composition operations by less nondeterministic or even completely deterministic ones. We show the validity of our solutions not only theoretically but also have evaluated them practically through implementation.}}, author = {{Mohr, Felix and Lettmann, Theodor and Kleine Büning, Hans}}, booktitle = {{Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA)}}, pages = {{154--161}}, title = {{{Reducing Nondeterminism in Automated Service Composition}}}, doi = {{10.1109/SOCA.2013.25}}, year = {{2013}}, } @article{515, abstract = {{The as a service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilised on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our approach of modelling this service composition and recommendation process as Markov decision process and of solving it by means of reinforcement learning. A case study serves as proof of concept.}}, author = {{Jungmann, Alexander and Kleinjohann, Bernd and Kleinjohann, Elisabeth}}, journal = {{International Journal of Business Process Integration and Management}}, number = {{4}}, pages = {{284--297}}, publisher = {{InderScience}}, title = {{{Learning Service Recommendations}}}, doi = {{10.1504/IJBPIM.2013.059135}}, year = {{2013}}, } @inproceedings{516, abstract = {{The as a Service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilized on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our general idea of modeling this service composition and recommendation process as Markov Decision Process and of solving it by means of Reinforcement Learning. A case study serves as proof of concept. }}, author = {{Jungmann, Alexander and Kleinjohann, Bernd}}, booktitle = {{Proceedings of the 10th IEEE International Conference on Services Computing (SCC)}}, pages = {{97--104}}, title = {{{Learning Recommendation System for Automated Service Composition}}}, doi = {{10.1109/SCC.2013.66}}, year = {{2013}}, } @misc{530, author = {{Buse, Dominik}}, publisher = {{Universität Paderborn}}, title = {{{Entwurf kooperativer Verhaltensweisen heterogener Roboter}}}, year = {{2013}}, }