Logo des Repositoriums

BISE 61(5) - October 2019

Autor*innen mit den meisten Dokumenten  

Auflistung nach:

Neueste Veröffentlichungen

1 - 7 von 7
  • Zeitschriftenartikel
    Data Portability on the Internet
    (Business & Information Systems Engineering: Vol. 61, No. 5, 2019) Wohlfarth, Michael
    Data portability allows users to transfer data between competing online services. As data gets increasingly valuable for online services and users alike, the enforcement of data portability within the European Union by the General Data Protection Regulation will have important ramifications for the competition in online markets. Thus, this paper develops a game-theoretic model to examine firms' strategic reaction to data portability and to identify the ensuing market outcomes. It can be shown, among others, that although data portability is designed to protect users, they may be hurt because market entrants have an incentive to increase the amount of collected data compared to a regime without data portability. However, profits for new services and total surplus increase if the costs for implementation are not too large. This likely improves innovation and service variety. Consequently, the results provide important insights and case-specific recommendations for managers and policy makers in data-driven online markets.
  • Zeitschriftenartikel
    Interview with Reinhold Achatz on 'Data Sovereignty and Data Ecosystems'
    (Business & Information Systems Engineering: Vol. 61, No. 5, 2019) Otto, Boris
  • Zeitschriftenartikel
    Privacy-Preserving Process Mining
    (Business & Information Systems Engineering: Vol. 61, No. 5, 2019) Mannhardt, Felix; Koschmider, Agnes; Baracaldo, Nathalie; Weidlich, Matthias; Michael, Judith
    Privacy regulations for data can be regarded as a major driver for data sovereignty measures. A specific example for this is the case of event data that is recorded by information systems during the processing of entities in domains such as e-commerce or health care. Since such data, typically available in the form of event log files, contains personalized information on the specific processed entities, it can expose sensitive information that may be traced back to individuals. In recent years, a plethora of methods have been developed to analyse event logs under the umbrella of process mining. However, the impact of privacy regulations on the technical design as well as the organizational application of process mining has been largely neglected. This paper set out to develop a protection model for event data privacy which applies the well-established notion of differential privacy. Starting from common assumptions about the event logs used in process mining, this paper presents potential privacy leakages and means to protect against them. The paper also shows at which stages of privacy leakages a protection model for event logs should be used. Relying on this understanding, the notion of differential privacy for process discovery methods is instantiated, i.e., algorithms that aim at the construction of a process model from an event log. The general feasibility of our approach is demonstrated by its application to two publicly available real-life events logs.
  • Zeitschriftenartikel
    Hybrid Intelligence
    (Business & Information Systems Engineering: Vol. 61, No. 5, 2019) Dellermann, Dominik; Ebel, Philipp; Söllner, Matthias; Leimeister, Jan Marco
  • Zeitschriftenartikel
    The New Era of Business Intelligence Applications: Building from a Collaborative Point of View
    (Business & Information Systems Engineering: Vol. 61, No. 5, 2019) Teruel, Miguel A.; Maté, Alejandro; Navarro, Elena; González, Pascual; Trujillo, Juan C.
    Collaborative business intelligence (BI) is widely embraced by enterprises as a way of making the most of their business processes. However, decision makers usually work in isolation without the knowledge or the time needed to obtain and analyze all the available information for making decisions. Unfortunately, collaborative BI is currently based on exchanging e-mails and documents between participants. As a result, information may be lost, participants may become disoriented, and the decision-making task may not yield the needed results. The authors propose a modeling language aimed at modeling and eliciting the goals and information needs of participants of collaborative BI systems. This approach is based on innovative methods to elicit and model collaborative systems and BI requirements. A controlled experiment was performed to validate this language, assessing its understandability, scalability, efficiency, and user satisfaction by analyzing two collaborative BI systems. By using the framework proposed in this work, clear guideless can be provided regarding: (1) collaborative tasks, (2) their participants, and (3) the information to be shared among them. By using the approach to design collaborative BI systems, practitioners may easily trace every element needed in the decision processes, avoiding the loss of information and facilitating the collaboration of the stakeholders of such processes.
  • Zeitschriftenartikel
    Discovering Data Quality Problems
    (Business & Information Systems Engineering: Vol. 61, No. 5, 2019) Zhang, Ruojing; Indulska, Marta; Sadiq, Shazia
    Existing methodologies for identifying data quality problems are typically user-centric, where data quality requirements are first determined in a top-down manner following well-established design guidelines, organizational structures and data governance frameworks. In the current data landscape, however, users are often confronted with new, unexplored datasets that they may not have any ownership of, but that are perceived to have relevance and potential to create value for them. Such repurposed datasets can be found in government open data portals, data markets and several publicly available data repositories. In such scenarios, applying top-down data quality checking approaches is not feasible, as the consumers of the data have no control over its creation and governance. Hence, data consumers - data scientists and analysts - need to be empowered with data exploration capabilities that allow them to investigate and understand the quality of such datasets to facilitate well-informed decisions on their use. This research aims to develop such an approach for discovering data quality problems using generic exploratory methods that can be effectively applied in settings where data creation and use is separated. The approach, named LANG, is developed through a Design Science approach on the basis of semiotics theory and data quality dimensions. LANG is empirically validated in terms of soundness of the approach, its repeatability and generalizability.
  • Zeitschriftenartikel
    Data Sovereignty and Data Space Ecosystems
    (Business & Information Systems Engineering: Vol. 61, No. 5, 2019) Jarke, Matthias; Otto, Boris; Ram, Sudha