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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
ZM2 A.03.05.1-

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Distributed merchandise management system

T. Göllner, J. Schwarz, S. Gottschalk, S. Sauer. Distributed merchandise management system, Patent US 2021/0081978 A1. 2021.

The invention describes a distributed merchandise management system, in which the client, retailer and the manufacturer are linked by a network. This is implemented by a cloud storage (105), the cloud storage (105) comprising a means (105 a) for storing data, a means for receiving first data from a first network node (110), the first data being associated with a physical object, a means for receiving request data from a second network node (120), a means for receiving second data from a third network node (130), the second data being associated with the first data and comprising at least one data piece adapted to change the first data depending on the received request data, a means for changing the first data based at least in part on the second data and the request data, and a means for sending a changed portion of the first data from the cloud storage (105) to the first network node (110).

Extending Business Model Development Tools with Consolidated Expert Knowledge (To Appear)

S. Gottschalk, J. Kirchhoff, G. Engels, in: Business Modeling and Software Design, 2021

Von datenbasierter zu datengetriebener Geschäftsmodellentwicklung: Ein Überblick über Software-Tools und deren Datennutzung

S. Gottschalk, E. Yigitbas, Gesellschaft für Informatik, 2021

Die kontinuierliche Weiterentwicklung des eigenen Geschäftsmodells ist für eine Organisation von entscheidender Bedeutung, um wettbewerbsfähig und somit nachhaltig erfolgreich zu bleiben. Während für die Entwicklung neuer Geschäftsmodelle häufig Workshops und einfache Software-Tools zur Visualisierung genutzt werden, wurden in der Forschung bereits erste Ansätze von datengetriebener Geschäftsmodellentwicklung (GME) vorgestellt. Diese Ansätze nutzen dabei Daten, Informationen oder auch Wissen aus internen und externen Unternehmensquellen, um den GME-Prozess zu unterstützen. Innerhalb dieses Beitrags zeigen wir einige Ansätze aus der aktuellen Literatur und analysieren wie ihre Datennutzung den GME-Prozess unterstützt. Weiterhin stellen wir mit dem BMDL Feature Modeler ein Tool vor, welches den GME-Prozess mit Expertenwissen unterstützt.


ProConAR: A Tool Support for Model-based AR Product Configuration

S. Gottschalk, E. Yigitbas, E. Schmidt, G. Engels, in: Human-Centered Software Engineering. HCSE 2020, Springer, 2020

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.

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

S. Gottschalk, E. Yigitbas, E. Schmidt, G. Engels, in: Human-Centered Software Engineering. HCSE 2020, Springer, 2020

Augmented Reality (AR) has recently found high attention in mobile shopping apps such as in domains like furniture or decoration. Here, the developers of the apps focus on the positioning of atomic 3D objects in the physical environment. With this focus, they neglect the configuration of multi-faceted 3D object composition according to the user needs and environmental constraints. To tackle these challenges, we present a model-based approach to support AR-assisted product con-figuration based on the concept of Dynamic Software Product Lines. Our approach splits products (e.g. table) into parts (e.g. tabletop, ta-ble legs, funnier) with their 3D objects and additional information (e.g. name, price). The possible products, which can be configured out of these parts, are stored in a feature model. At runtime, this feature model can be used to configure 3D object compositions out of the product parts and adapt to user needs and environmental constraints. The benefits of this approach are demonstrated by a case study of configuring modular kitchens with the help of a prototypical mobile-based implementation.

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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