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Sebastian Gottschalk, M.Sc.

 Sebastian Gottschalk, M.Sc.

Datenbank- und Informationssysteme

Doktorand - Wissenschaftlicher Mitarbeiter

Software Innovation Campus Paderborn (SICP)

Wissenschaftlicher Mitarbeiter

Sonderforschungsbereich 901

Wissenschaftlicher Mitarbeiter

+49 5251 60-6588
Fürstenallee 11
33102 Paderborn

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ProConAR: A Tool Support for Model-based AR Product Configuration (to appear)

S. Gottschalk, E. Yigitbas, E. Schmidt, G. Engels, in: Proceedings of the 8th International Working Conference on Human-Centered Software Engineering (HCSE'20), 2020

Hypothesis-driven Adaptation of Business Models based on Product Line Engineering

S. Gottschalk, F. Rittmeier, G. Engels, in: Proceedings of the 22nd IEEE International Conference on Business Informatics, IEEE, 2020

The continuous innovation of its business models is an important task for a company to stay competitive. During this process, the company has to validate various hypotheses about its business models by adapting to uncertain and changing customer needs effectively and efficiently. This adaptation, in turn, can be supported by the concept of Software Product Lines (SPLs). SPLs reduce the time to market by deriving products for customers with changing requirements using a common set of features, structured as a feature model. Analogously, we support the process of business model adaptation by applying the engineering process of SPLs to the structure of the Business Model Canvas (BMC). We call this concept a Business Model Decision Line (BMDL). The BMDL matches business domain knowledge in the form of a feature model with customer needs to derive hypotheses about the business model together with experiments for validation. Our approach is effective by providing a comprehensive overview of possible business model adaptations and efficient by reusing experiments for different hypotheses. We implement our approach in a tool and illustrate the usefulness with an example of developing business models for a mobile application.

Model-based Product Configuration in Augmented Reality Applications (to appear)

S. Gottschalk, E. Yigitbas, E. Schmidt, G. Engels, in: Proceedings of the 8th International Working Conference on Human-Centered Software Engineering (HCSE'20), 2020

Model-based Hypothesis Engineering for Supporting Adaptation to Uncertain Customer Needs

S. Gottschalk, E. Yigitbas, G. Engels, in: Business Modeling and Software Design, Springer International Publishing, 2020, pp. 276-286

To build successful products, the developers have to adapt their product features and business models to uncertain customer needs. This adaptation is part of the research discipline of Hypotheses Engineering (HE) where customer needs can be seen as hypotheses that need to be tested iteratively by conducting experiments together with the customer. So far, modeling support and associated traceability of this iterative process are missing. Both, in turn, are important to document the adaptation to the customer needs and identify experiments that provide most evidence to the customer needs. To target this issue, we introduce a model-based HE approach with a twofold contribution: First, we develop a modeling language that models hypotheses and experiments as interrelated hierarchies together with a mapping between them. While the hypotheses are labeled with a score level of their current evidence, the experiments are labeled with a score level of maximum evidence that can be achieved during conduction. Second, we provide an iterative process to determine experiments that offer the most evidence improvement to the modeled hypotheses. We illustrate the usefulness of the approach with an example of testing the business model of a mobile application.


Intertwined Development of Business Model and Product Functions for Mobile Applications: A Twin Peak Feature Modeling Approach

S. Gottschalk, F. Rittmeier, G. Engels, in: Software Business, Springer International Publishing, 2019, pp. 192-207

Mobile app stores like Apple's AppStore or Google's PlayStore are highly competitive markets for third-party developers wanting to develop successful applications. During the development process, many developers focus on the multitude of product functions but neglect the business model as an equally important part. As a result, developers often fail to meet customer needs, leading to unnecessary development costs and poor market penetration. This, in turn, raises the question of how we intertwine the business model and product functions during the development process to ensure a better alignment between the two. In this paper, we show this intertwined development by adapting the concept of Twin Peaks to the business model and product functions. Based on feature modeling as an abstraction layer, we introduce the concept of a Business Model Decision Line (BMDL) to structure the business model decisions and their relation to product functions structured in a Software Product Line (SPL). The basis of our feature models is the analysis of top listed applications in the app stores of Apple and Google. To create and modify both models, we provide an incremental feature structuring and iterative feature selection process. This combination of abstraction layer and development process supports third-party developers to build successful applications both from a business and a product perspective.

Verteiltes Warenwirtschaftssystem [Distributed Warehouse System]

T. Göllner, J. Schwarz, S. Gottschalk, S. Sauer. Verteiltes Warenwirtschaftssystem [Distributed Warehouse System], Patent DE 10 2018 206 390. 2019.

Beschrieben ist ein verteiltes Warenwirtschaftssystem, bei dem der Kunde, Händler, und der Hersteller vernetzt sind. Dies wird bewerkstelligt durch einen Cloud-Speicher (105), der Cloud-Speicher (105) aufweisend ein Mittel zum Speichern (105a) von Daten, ein Mittel zum Empfangen von ersten Daten von einem ersten Netzwerkteilnehmer (110), wobei die ersten Daten zugehörig sind zu einem physischen Objekt, ein Mittel zum Empfangen von Anfragedaten von einem zweiten Netzwerkteilnehmer (120), ein Mittel zum Empfangen von zweiten Daten von einem dritten Netzwerkteilnehmer (130), wobei die zweiten Daten zugehörig sind zu den ersten Daten und zumindest ein Datum aufweisen, welches angepasst ist, die ersten Daten zu ändern in Abhängigkeit der empfangenen Anfragedaten, ein Mittel zum Ändern der ersten Daten basierend zumindest im Teil auf den zweiten Daten und den Anfragedaten und ein Mittel zum Senden eines geänderten Teils der ersten Daten von dem Cloud-Speicher (105) an den ersten Netzwerkteilnehmer (110).

Business Models of Store-Oriented Software Ecosystems: A Variability Modeling Approach

S. Gottschalk, F. Rittmeier, G. Engels, in: Business Modeling and Software Design, Springer International Publishing, 2019, pp. 153-169

In the last years, store-oriented software ecosystems are gaining more and more attention from a business perspective. In these ecosystems, third-party developers upload extensions to a store which can be downloaded by end users. While the functional scope of such ecosystems is relatively similar, the underlying business models differ greatly in and between their different product domains (e.g. Mobile Phone, Smart TV). This variability, in turn, makes it challenging for store providers to find a business model that fits their own needs. To handle this variability, we introduce the Business Variability Model (BVM) for modeling business model decisions. The basis of these decisions is the analysis of 60 store-oriented software ecosystems in eight different product domains. We map their business model decisions to the Business Model Canvas, condense them to a variability model and discuss particular variants and their dependencies. Our work provides store providers a new approach for modeling business model decisions together with insights of existing business models. This, in turn, supports them in creating new and improving existing business models.

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