@phdthesis{28683,
  abstract     = {{In den letzten Jahren haben sich Software-Ökosysteme als neue, erfolgreiche Geschäftsform etabliert. Unternehmen agieren hierbei als Anbieter von Software-Plattformen, auf denen Drittanbieter Softwarelösungen für den Markt anbieten können.  Etablierte Beispiele sind hierbei sogenannte App-Stores, die z.B. von Google oder Apple angeboten werden.

Beim Aufbau von Software-Ökosystemen müssen vom Plattformanbieter viele architektonische Entwurfsentscheidungen getroffen werden. Bisher gibt es keine Architekturrichtlinien und -werkzeuge, die den Entwurf einer Ökosystemarchitektur unterstützen. Dadurch fehlt hier systematisches, wiederverwendbares Wissen. Plattformanbieter müssen auf ad-hoc Entscheidungen zurückgreifen. Dies kann dann zu Problemen im Betrieb der Software-Plattformen führen, zu erhöhten Ausfallrisiken und Mehrkosten.

Der Mangel an Architekturwissen manifestiert sich konkret in zwei Gruppen von Herausforderungen: Erstens fehlt eine Wissensbasis zu Architekturalternativen und zweitens fehlt es an methodischem Wissen zu Entwicklung und Betrieb von Software-Ökosystemen. Eine Architekturwissensbasis würde Orientierungshilfen zu den Bestandteilen von Software-Ökosystemen und deren Abhängigkeiten geben, während methodisches Wissen die Erstellung dieser Systeme erleichtern würde.

In der Dissertation werden diese Herausforderungen durch die Entwicklung des Frameworks SecoArc für die Modellierung von Software-Ökosystemen angegangen. Der Beitrag der Dissertation ist zweifach: 
1.	Das SecoArc-Framework umfasst eine Architekturwissensbasis, die wiederverwendbare Architekturentwurfsentscheidungen
von Software-Ökosystemen enthält. Die Wissensbasis wurde entwickelt, indem das Architekturwissen bestehender Ökosysteme sowie aus existierender Fachliteratur ermittelt wurde und in einer Produktlinie für Software-Ökosysteme konsolidiert wurde. Die Produktlinie umfasst architektonische Gemeinsamkeiten und Variabilitäten von Software-Ökosystemen. 
2.	Das SecoArc-Framework liefert methodisches Wissen, um die Ökosystemarchitektur in Modellen zu entwerfen und zu analysieren. Dieses Wissen wurde entwickelt, indem drei Architekturmuster identifiziert wurden. Jedes Muster erfasst unterschiedliche Beziehungen zwischen architektonischen Entwurfsentscheidungen zu den Qualitätsmerkmalen einer Ökosystemgesundheit und der Erreichung von Geschäftszielen. 

Die Architekturmuster und die Produktlinie wurden dazu genutzt, ein Modellierungsframework zu entwickeln und in Form eines Prototypen umzusetzen, welches einen Entwurfsprozess, eine Modellierungssprache und eine Architekturanalysetechnik umfasst. Es erleichtert das Modellieren, Analysieren und Vergleichen von Ökosystemarchitekturen.

Die Ergebnisse der Dissertation wurden im Rahmen von zwei Studien evaluiert. In der ersten Validierungsstudie wurden das Framework sowie der Prototyp verwendet, um zwei alternative Ökosystemarchitekturen zu entwerfen und zu analysieren. In der zweiten Studie wurde eine Analyse von existierenden Ökosystemen basierend auf den architektonischen Variabilitäten des Frameworks durchgeführt.}},
  author       = {{Schwichtenberg, Bahar}},
  keywords     = {{Enterprise Architecture, Architectural Design Decisions, Open Platforms}},
  title        = {{{Modeling and Analyzing Software Ecosystems}}},
  doi          = {{10.17619/UNIPB/1-1270 }},
  year         = {{2021}},
}

@inproceedings{29566,
  author       = {{Bobolz, Jan and Eidens, Fabian and Krenn, Stephan and Ramacher, Sebastian and Samelin, Kai}},
  booktitle    = {{Cryptology and Network Security}},
  isbn         = {{9783030925475}},
  issn         = {{0302-9743}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Issuer-Hiding Attribute-Based Credentials}}},
  doi          = {{10.1007/978-3-030-92548-2_9}},
  year         = {{2021}},
}

@inproceedings{21813,
  author       = {{Hansmeier, Tim and Platzner, Marco}},
  booktitle    = {{GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-8351-6}},
  location     = {{Lille, France}},
  pages        = {{1639–1647}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{{An Experimental Comparison of Explore/Exploit Strategies for the Learning Classifier System XCS}}},
  doi          = {{10.1145/3449726.3463159}},
  year         = {{2021}},
}

@techreport{35889,
  abstract     = {{Network and service coordination is important to provide modern services consisting of multiple interconnected components, e.g., in 5G, network function virtualization (NFV), or cloud and edge computing. In this paper, I outline my dissertation research, which proposes six approaches to automate such network and service coordination. All approaches dynamically react to the current demand and optimize coordination for high service quality and low costs. The approaches range from centralized to distributed methods and from conventional heuristic algorithms and mixed-integer linear programs to machine learning approaches using supervised and reinforcement learning. I briefly discuss their main ideas and advantages over other state-of-the-art approaches and compare strengths and weaknesses.}},
  author       = {{Schneider, Stefan Balthasar}},
  keywords     = {{nfv, coordination, machine learning, reinforcement learning, phd, digest}},
  title        = {{{Conventional and Machine Learning Approaches for Network and Service Coordination}}},
  year         = {{2021}},
}

@article{17860,
  abstract     = {{Purpose
The purpose of this paper is to identify strategic options and challenges that arise when an industrial firm moves from providing smart service toward providing a platform.

Design/methodology/approach
This conceptual study takes on a multidisciplinary research perspective that integrates concepts, theories and insights from service management and marketing, information systems and platform economics.

Findings
The paper outlines three platform types – smart data platform, smart product platform and matching platform – as strategic options for firms that wish to evolve from smart service providers to platform providers.

Research limitations/implications
Investigating smart service platforms calls for launching interdisciplinary research initiatives. Promising research avenues are outlined to span boundaries that separate different research disciplines today.

Practical implications
Managing a successful transition from providing smart service toward providing a platform requires making significant investments in IT, platform-related capabilities and skills, as well as implement new approaches toward relationship management and brand-building.

Originality/value
The findings described in this paper are valuable to researchers in multiple disciplines seeking to develop and to justify theory related to platforms in industrial scenarios.}},
  author       = {{Beverungen, Daniel and Kundisch, Dennis and Wünderlich, Nancy}},
  issn         = {{507-532}},
  journal      = {{Journal of Service Management}},
  keywords     = {{Smart service, Platform, Interdisciplinary research, Manufacturing company, Smart service provider, Platform economics, Information systems, Multi-sided markets, Business-to-business (B2B) markets}},
  number       = {{4}},
  pages        = {{507--532}},
  publisher    = {{Emerald Insight}},
  title        = {{{Transforming into a Platform Provider: Strategic Options for Industrial Smart Service Providers}}},
  doi          = {{10.1108/JOSM-03-2020-0066}},
  volume       = {{32}},
  year         = {{2021}},
}

@misc{45236,
  author       = {{N., N.}},
  title        = {{{Design and Implementation of a Crowd-based Prototype Validation Platform}}},
  year         = {{2021}},
}

@misc{45239,
  author       = {{N., N.}},
  title        = {{{Lightweight Process Engine for Situation-specific Development of Business Models for Digital Platforms}}},
  year         = {{2021}},
}

@misc{45240,
  author       = {{N., N.}},
  title        = {{{Development and Evaluation of a Multi Platform Approach for Augmented Reality Product Configuration}}},
  year         = {{2021}},
}

@misc{45238,
  author       = {{N., N.}},
  title        = {{{Model-based Feature Backlog Synchronization for Dual-Track Development Methods}}},
  year         = {{2021}},
}

@phdthesis{24884,
  author       = {{Szopinski, Daniel}},
  title        = {{{Essays on Modeling Languages and Software Tools for Business Model Innovation: Theory and Empirical Evidence}}},
  doi          = {{10.17619/UNIPB/1-1647 }},
  year         = {{2021}},
}

@inproceedings{19551,
  author       = {{Kurek, Rafael}},
  booktitle    = {{Information Security and Privacy - 25th Australasian Conference, {ACISP} 2020, Perth, WA, Australia, November 30 - December 2, 2020, Proceedings}},
  editor       = {{K. Liu, Joseph and Cui, Hui}},
  pages        = {{330--349}},
  publisher    = {{Springer}},
  title        = {{{Efficient Forward-Secure Threshold Public Key Encryption}}},
  doi          = {{10.1007/978-3-030-55304-3\_17}},
  volume       = {{12248}},
  year         = {{2020}},
}

@inproceedings{19553,
  author       = {{Kurek, Rafael}},
  booktitle    = {{Advances in Information and Computer Security - 15th International Workshop on Security, {IWSEC} 2020, Fukui, Japan, September 2-4, 2020, Proceedings}},
  editor       = {{Aoki, Kazumaro and Kanaoka, Akira}},
  pages        = {{239--260}},
  publisher    = {{Springer}},
  title        = {{{Efficient Forward-Secure Threshold Signatures}}},
  doi          = {{10.1007/978-3-030-58208-1\_14}},
  volume       = {{12231}},
  year         = {{2020}},
}

@inproceedings{19606,
  abstract     = {{Mobile shopping apps have been using Augmented Reality (AR) in the last years to place their products in the environment of the customer. While this is possible with atomic 3D objects, there is is still a lack in the runtime configuration of 3D object compositions based on user needs and environmental constraints. For this, we previously developed an approach for model-based AR-assisted product configuration based on the concept of Dynamic Software Product Lines. In this demonstration paper, we present the corresponding tool support ProConAR in the form of a Product Modeler and a Product Configurator. While the Product Modeler is an Angular web app that splits products (e.g. table) up into atomic parts (e.g. tabletop, table legs, funnier) and saves it within a configuration model, the Product Configurator is an Android client that uses the configuration model to place different product configurations within the environment of the customer. We show technical details of our ready to use tool-chain ProConAR by describing its implementation and usage as well as pointing out future research directions.}},
  author       = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}},
  booktitle    = {{Human-Centered Software Engineering. HCSE 2020}},
  editor       = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}},
  keywords     = {{Product Configuration, Augmented Reality, Model-based, Tool Support}},
  location     = {{Eindhoven}},
  publisher    = {{Springer}},
  title        = {{{ProConAR: A Tool Support for Model-based AR Product Configuration}}},
  doi          = {{10.1007/978-3-030-64266-2_14}},
  volume       = {{12481}},
  year         = {{2020}},
}

@inproceedings{19607,
  abstract     = {{Modern services consist of modular, interconnected
components, e.g., microservices forming a service mesh. To
dynamically adjust to ever-changing service demands, service
components have to be instantiated on nodes across the network.
Incoming flows requesting a service then need to be routed
through the deployed instances while considering node and link
capacities. Ultimately, the goal is to maximize the successfully
served flows and Quality of Service (QoS) through online service
coordination. Current approaches for service coordination are
usually centralized, assuming up-to-date global knowledge and
making global decisions for all nodes in the network. Such global
knowledge and centralized decisions are not realistic in practical
large-scale networks.

To solve this problem, we propose two algorithms for fully
distributed service coordination. The proposed algorithms can be
executed individually at each node in parallel and require only
very limited global knowledge. We compare and evaluate both
algorithms with a state-of-the-art centralized approach in extensive
simulations on a large-scale, real-world network topology.
Our results indicate that the two algorithms can compete with
centralized approaches in terms of solution quality but require
less global knowledge and are magnitudes faster (more than
100x).}},
  author       = {{Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}},
  booktitle    = {{IEEE International Conference on Network and Service Management (CNSM)}},
  keywords     = {{distributed management, service coordination, network coordination, nfv, softwarization, orchestration}},
  publisher    = {{IEEE}},
  title        = {{{Every Node for Itself: Fully Distributed Service Coordination}}},
  year         = {{2020}},
}

@inproceedings{19609,
  abstract     = {{Modern services comprise interconnected components,
e.g., microservices in a service mesh, that can scale and
run on multiple nodes across the network on demand. To process
incoming traffic, service components have to be instantiated and
traffic assigned to these instances, taking capacities and changing
demands into account. This challenge is usually solved with
custom approaches designed by experts. While this typically
works well for the considered scenario, the models often rely
on unrealistic assumptions or on knowledge that is not available
in practice (e.g., a priori knowledge).

We propose a novel deep reinforcement learning approach that
learns how to best coordinate services and is geared towards
realistic assumptions. It interacts with the network and relies on
available, possibly delayed monitoring information. Rather than
defining a complex model or an algorithm how to achieve an
objective, our model-free approach adapts to various objectives
and traffic patterns. An agent is trained offline without expert
knowledge and then applied online with minimal overhead. Compared
to a state-of-the-art heuristic, it significantly improves flow
throughput and overall network utility on real-world network
topologies and traffic traces. It also learns to optimize different
objectives, generalizes to scenarios with unseen, stochastic traffic
patterns, and scales to large real-world networks.}},
  author       = {{Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}},
  booktitle    = {{IEEE International Conference on Network and Service Management (CNSM)}},
  keywords     = {{self-driving networks, self-learning, network coordination, service coordination, reinforcement learning, deep learning, nfv}},
  publisher    = {{IEEE}},
  title        = {{{Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}}},
  year         = {{2020}},
}

@inproceedings{19739,
  author       = {{Szopinski, Daniel}},
  booktitle    = {{Proceedings of the 15th International Conference on Design Science Research in Information Systems and Technology (DESRIST)}},
  location     = {{Virtual Conference/Workshop}},
  title        = {{{Active Business Model Development Tools: Design Requirements}}},
  year         = {{2020}},
}

@inproceedings{19741,
  author       = {{Szopinski, Daniel}},
  booktitle    = {{Proceedings of the 41st International Conference on Information Systems (ICIS)}},
  location     = {{Virtual Conference/Workshop}},
  title        = {{{Exploring design principles for stimuli in business model development tools}}},
  year         = {{2020}},
}

@inproceedings{19782,
  author       = {{Müller, Michelle and Neumann, Jürgen and Gutt, Dominik and Kundisch, Dennis}},
  booktitle    = {{Proceedings of the 41th International Conference on Information Systems (ICIS)}},
  location     = {{Virtual Conference/Workshop}},
  title        = {{{Toss a Coin to your Host - How Guests End up Paying for the Cost of Regulatory Policies}}},
  year         = {{2020}},
}

@phdthesis{24710,
  author       = {{Kurek, Rafael}},
  title        = {{{Efficient Cryptographic Constructions with Strong Security Guarantees}}},
  year         = {{2020}},
}

@inbook{21396,
  abstract     = {{Verifiable random functions (VRFs) are essentially digital signatures with additional properties, namely verifiable uniqueness and pseudorandomness, which make VRFs a useful tool, e.g., to prevent enumeration in DNSSEC Authenticated Denial of Existence and the CONIKS key management system, or in the random committee selection of the Algorand blockchain.

Most standard-model VRFs rely on admissible hash functions (AHFs) to achieve security against adaptive attacks in the standard model. Known AHF constructions are based on error-correcting codes, which yield asymptotically efficient constructions. However, previous works do not clarify how the code should be instantiated concretely in the real world. The rate and the minimal distance of the selected code have significant impact on the efficiency of the resulting cryptosystem, therefore it is unclear if and how the aforementioned constructions can be used in practice.

First, we explain inherent limitations of code-based AHFs. Concretely, we assume that even if we were given codes that achieve the well-known Gilbert-Varshamov or McEliece-Rodemich-Rumsey-Welch bounds, existing AHF-based constructions of verifiable random functions (VRFs) can only be instantiated quite inefficiently. Then we introduce and construct computational AHFs (cAHFs). While classical AHFs are information-theoretic, and therefore work even in presence of computationally unbounded adversaries, cAHFs provide only security against computationally bounded adversaries. However, we show that cAHFs can be instantiated significantly more efficiently. Finally, we use our cAHF to construct the currently most efficient verifiable random function with full adaptive security in the standard model.}},
  author       = {{Jager, Tibor and Niehues, David}},
  booktitle    = {{Lecture Notes in Computer Science}},
  isbn         = {{9783030384708}},
  issn         = {{0302-9743}},
  keywords     = {{Admissible hash functions, Verifiable random functions, Error-correcting codes, Provable security}},
  location     = {{Waterloo, Canada}},
  title        = {{{On the Real-World Instantiability of Admissible Hash Functions and Efficient Verifiable Random Functions}}},
  doi          = {{10.1007/978-3-030-38471-5_13}},
  year         = {{2020}},
}