@unpublished{17714, author = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}}, title = {{{Automated machine learning service composition}}}, year = {{2018}}, } @misc{5693, author = {{Graf, Helena}}, publisher = {{Universität Paderborn}}, title = {{{Ranking of Classification Algorithms in AutoML}}}, year = {{2018}}, } @misc{5936, author = {{Scheibl, Manuel}}, publisher = {{Universität Paderborn}}, title = {{{Learning about learning curves from dataset properties}}}, year = {{2018}}, } @inbook{6423, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{Discovery Science}}, isbn = {{9783030017705}}, issn = {{0302-9743}}, pages = {{161--175}}, publisher = {{Springer International Publishing}}, title = {{{Preference-Based Reinforcement Learning Using Dyad Ranking}}}, doi = {{10.1007/978-3-030-01771-2_11}}, year = {{2018}}, } @inproceedings{115, abstract = {{Whenever customers have to decide between different instances of the same product, they are interested in buying the best product. In contrast, companies are interested in reducing the construction effort (and usually as a consequence thereof, the quality) to gain profit. The described setting is widely known as opposed preferences in quality of the product and also applies to the context of service-oriented computing. In general, service-oriented computing emphasizes the construction of large software systems out of existing services, where services are small and self-contained pieces of software that adhere to a specified interface. Several implementations of the same interface are considered as several instances of the same service. Thereby, customers are interested in buying the best service implementation for their service composition wrt. to metrics, such as costs, energy, memory consumption, or execution time. One way to ensure the service quality is to employ certificates, which can come in different kinds: Technical certificates proving correctness can be automatically constructed by the service provider and again be automatically checked by the user. Digital certificates allow proof of the integrity of a product. Other certificates might be rolled out if service providers follow a good software construction principle, which is checked in annual audits. Whereas all of these certificates are handled differently in service markets, what they have in common is that they influence the buying decisions of customers. In this paper, we review state-of-the-art developments in certification with respect to service-oriented computing. We not only discuss how certificates are constructed and handled in service-oriented computing but also review the effects of certificates on the market from an economic perspective.}}, author = {{Jakobs, Marie-Christine and Krämer, Julia and van Straaten, Dirk and Lettmann, Theodor}}, booktitle = {{The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION)}}, editor = {{Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas Prinz}}, pages = {{7--12}}, title = {{{Certification Matters for Service Markets}}}, year = {{2017}}, } @misc{5694, author = {{Schnitker, Nino Noel}}, publisher = {{Universität Paderborn}}, title = {{{Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies}}}, year = {{2017}}, } @misc{5724, author = {{Hetzer, Alexander and Tornede, Tanja}}, publisher = {{Universität Paderborn}}, title = {{{Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction}}}, year = {{2017}}, } @inproceedings{71, abstract = {{Today, software verification tools have reached the maturity to be used for large scale programs. Different tools perform differently well on varying code. A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice. Such rankings can, however, so far only be obtained by running all considered tools on the program.In this paper, we present a machine learning approach to predicting rankings of tools on programs. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for programs. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6).}}, author = {{Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, booktitle = {{Proceedings of the 3rd International Workshop on Software Analytics}}, pages = {{23--26}}, title = {{{Predicting Rankings of Software Verification Tools}}}, doi = {{10.1145/3121257.3121262}}, year = {{2017}}, } @inproceedings{1180, abstract = {{These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{27th Workshop Computational Intelligence}}, location = {{Dortmund}}, title = {{{Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}}}, year = {{2017}}, } @article{190, abstract = {{Today, software components are provided by global markets in the form of services. In order to optimally satisfy service requesters and service providers, adequate techniques for automatic service matching are needed. However, a requester’s requirements may be vague and the information available about a provided service may be incomplete. As a consequence, fuzziness is induced into the matching procedure. The contribution of this paper is the development of a systematic matching procedure that leverages concepts and techniques from fuzzy logic and possibility theory based on our formal distinction between different sources and types of fuzziness in the context of service matching. In contrast to existing methods, our approach is able to deal with imprecision and incompleteness in service specifications and to inform users about the extent of induced fuzziness in order to improve the user’s decision-making. We demonstrate our approach on the example of specifications for service reputation based on ratings given by previous users. Our evaluation based on real service ratings shows the utility and applicability of our approach.}}, author = {{Platenius, Marie Christin and Shaker, Ammar and Becker, Matthias and Hüllermeier, Eyke and Schäfer, Wilhelm}}, journal = {{IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017}}, number = {{8}}, pages = {{739--759}}, publisher = {{IEEE}}, title = {{{Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic}}}, doi = {{10.1109/TSE.2016.2632115}}, year = {{2016}}, } @inproceedings{225, abstract = {{Image Processing is fundamental for any camera-based vision system. In order to automate the prototyping process of image processing solutions to some extend, we propose a holistic, adaptive approach that comprises concepts for specification, composition, recommendation, execution, and rating of image processing functionality. The fundamental idea is to realize image processing applications according to Service-oriented Computing design principles. That is, distinct image processing functionality is encapsulated in terms of stateless services. Services are then used as building blocks for more complex image processing functionality. To automatically compose complex image processing functionality, our proposed approach incorporates a flexible, Artificial Intelligence planning-based forward search algorithm. Decision-making between alternative composition steps is supported by a learning recommendation system, which keeps track of valid composition steps by automatically constructing a composition grammar. In addition, it adapts to solutions of high quality by means of feedback-based Reinforcement Learning techniques. A concrete use case serves as proof of concept and demonstrates the feasibility of our holistic, adaptive approach.}}, author = {{Jungmann, Alexander and Kleinjohann, Bernd}}, booktitle = {{Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, pages = {{1----8}}, title = {{{A Holistic and Adaptive Approach for Automated Prototyping of Image Processing Functionality}}}, doi = {{10.1109/ETFA.2016.7733522}}, year = {{2016}}, } @inproceedings{218, abstract = {{In the Image Processing domain, automated generation of complex Image Processing functionality is highly desirable; e.g., for rapid prototyping. Service composition techniques, in turn, facilitate automated generation of complex functionality based on building blocks in terms of services. For that reason, we aim for transferring the Service Composition paradigm into the Image Processing domain. In this paper, we present our symbolic composition approach that enables us to automatically generate Image Processing applications. Functionality of Image Processing services is described by means of a variant of first-order logic, which grounds on domain knowledge operationalized in terms of ontologies. A Petri-net formalism serves as basis for modeling data-flow of services and composed services. A planning-based composition algorithm automatically composes complex data-flow for a required functionality. A brief evaluation serves as proof of concept.}}, author = {{Jungmann, Alexander and Kleinjohann, Bernd}}, booktitle = {{Proceedings of the 13th IEEE International Conference on Services Computing (SCC)}}, pages = {{106----113}}, title = {{{Automatic Composition of Service-based Image Processing Applications}}}, doi = {{10.1109/SCC.2016.21}}, year = {{2016}}, } @phdthesis{140, author = {{Jungmann, Alexander}}, publisher = {{Universität Paderborn}}, title = {{{Towards On-The-Fly Image Processing}}}, year = {{2016}}, } @phdthesis{141, author = {{Mohr, Felix}}, publisher = {{Universität Paderborn}}, title = {{{Towards Automated Service Composition Under Quality Constraints}}}, doi = {{10.17619/UNIPB/1-171}}, year = {{2016}}, } @inproceedings{280, abstract = {{The Collaborative Research Centre "On-The-Fly Computing" works on foundations and principles for the vision of the Future Internet. It proposes the paradigm of On-The-Fly Computing, which tackles emerging worldwide service markets. In these markets, service providers trade software, platform, and infrastructure as a service. Service requesters state requirements on services. To satisfy these requirements, the new role of brokers, who are (human) actors building service compositions on the fly, is introduced. Brokers have to specify service compositions formally and comprehensively using a domain-specific language (DSL), and to use service matching for the discovery of the constituent services available in the market. The broker's choice of the DSL and matching approaches influences her success of building compositions as distinctive properties of different service markets play a significant role. In this paper, we propose a new approach of engineering a situation-specific DSL by customizing a comprehensive, modular DSL and its matching for given service market properties. This enables the broker to create market-specific composition specifications and to perform market-specific service matching. As a result, the broker builds service compositions satisfying the requester's requirements more accurately. We evaluated the presented concepts using case studies in service markets for tourism and university management.}}, author = {{Arifulina, Svetlana and Platenius, Marie Christin and Mohr, Felix and Engels, Gregor and Schäfer, Wilhelm}}, booktitle = {{Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet}}, pages = {{333----340}}, title = {{{Market-Specific Service Compositions: Specification and Matching}}}, doi = {{10.1109/SERVICES.2015.58}}, year = {{2015}}, } @inproceedings{245, abstract = {{In cloud computing, software architects develop systems for virtually unlimited resources that cloud providers account on a pay-per-use basis. Elasticity management systems provision these resources autonomously to deal with changing workload. Such changing workloads call for new objective metrics allowing architects to quantify quality properties like scalability, elasticity, and efficiency, e.g., for requirements/SLO engineering and software design analysis. In literature, initial metrics for these properties have been proposed. However, current metrics lack a systematic derivation and assume knowledge of implementation details like resource handling. Therefore, these metrics are inapplicable where such knowledge is unavailable.To cope with these lacks, this short paper derives metrics for scalability, elasticity, and efficiency properties of cloud computing systems using the goal question metric (GQM) method. Our derivation uses a running example that outlines characteristics of cloud computing systems. Eventually, this example allows us to set up a systematic GQM plan and to derive an initial set of six new metrics. We particularly show that our GQM plan allows to classify existing metrics.}}, author = {{Becker, Matthias and Lehrig, Sebastian and Becker, Steffen}}, booktitle = {{Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering}}, pages = {{169----174}}, title = {{{Systematically Deriving Quality Metrics for Cloud Computing Systems}}}, doi = {{10.1145/2668930.2688043}}, year = {{2015}}, } @article{323, abstract = {{On-the-fly composition of service-based software solutions is still a challenging task. Even more challenges emerge when facing automatic service composition in markets of composed services for end users. In this paper, we focus on the functional discrepancy between “what a user wants” specified in terms of a request and “what a user gets” when executing a composed service. To meet the challenge of functional discrepancy, we propose the combination of existing symbolic composition approaches with machine learning techniques. We developed a learning recommendation system that expands the capabilities of existing composition algorithms to facilitate adaptivity and consequently reduces functional discrepancy. As a representative of symbolic techniques, an Artificial Intelligence planning based approach produces solutions that are correct with respect to formal specifications. Our learning recommendation system supports the symbolic approach in decision-making. Reinforcement Learning techniques enable the recommendation system to adjust its recommendation strategy over time based on user ratings. We implemented the proposed functionality in terms of a prototypical composition framework. Preliminary results from experiments conducted in the image processing domain illustrate the benefit of combining both complementary techniques.}}, author = {{Jungmann, Alexander and Mohr, Felix}}, journal = {{Journal of Internet Services and Applications}}, number = {{1}}, pages = {{1--18}}, publisher = {{Springer}}, title = {{{An approach towards adaptive service composition in markets of composed services}}}, doi = {{10.1186/s13174-015-0022-8}}, year = {{2015}}, } @inproceedings{324, abstract = {{Services are self-contained software components that can beused platform independent and that aim at maximizing software reuse. Abasic concern in service oriented architectures is to measure the reusabilityof services. One of the most important qualities is the functionalreusability, which indicates how relevant the task is that a service solves.Current metrics for functional reusability of software, however, have verylittle explanatory power and do not accomplish this goal.This paper presents a new approach to estimate the functional reusabilityof services based on their relevance. To this end, it denes the degreeto which a service enables the execution of other services as its contri-bution. Based on the contribution, relevance of services is dened as anestimation for their functional reusability. Explanatory power is obtainedby normalizing relevance values with a reference service. The applicationof the metric to a service test set conrms its supposed capabilities.}}, author = {{Mohr, Felix}}, booktitle = {{Proceedings of the 14th International Conference on Software Reuse (ICSR)}}, pages = {{298----313}}, title = {{{A Metric for Functional Reusability of Services}}}, doi = {{10.1007/978-3-319-14130-5_21}}, year = {{2015}}, } @article{3343, abstract = {{In this paper we consider an extended variant of query learning where the hidden concept is embedded in some Boolean circuit. This additional processing layer modifies query arguments and answers by fixed transformation functions which are known to the learner. For this scenario, we provide a characterization of the solution space and an ordering on it. We give a compact representation of the minimal and maximal solutions as quantified Boolean formulas and we adapt the original algorithms for exact learning of specific classes of propositional formulas.}}, author = {{Bubeck, Uwe and Kleine Büning, Hans}}, issn = {{0004-3702}}, journal = {{Artificial Intelligence}}, keywords = {{Query learning, Propositional logic}}, pages = {{246 -- 257}}, publisher = {{Elsevier}}, title = {{{Learning Boolean Specifications}}}, doi = {{10.1016/j.artint.2015.09.003}}, year = {{2015}}, } @inproceedings{315, abstract = {{In this paper, we introduce an approach for combining embedded systems with Service-oriented Computing techniques based on a concrete application scenario from the robotics domain. Our proposed Service-oriented Architecture allows for incorporating computational expensive functionality as services into a distributed computing environment. Furthermore, our framework facilitates a seamless integration of embedded systems such as robots as service providers into the computing environment. The entire communication is based on so-called recipes, which can be interpreted as autonomous messages that contain all necessary information for executing compositions of services.}}, author = {{Jungmann, Alexander and Jatzkowski, Jan and Kleinjohann, Bernd}}, booktitle = {{Proceedings of the 5th IFIP International Embedded Systems Symposium}}, title = {{{Combining Service-oriented Computing with Embedded Systems - A Robotics Case Study}}}, year = {{2015}}, }