Pierre Maier

Academic Staff

Pierre Maier, M. Sc.

Room:
R09 R04 H38
Phone:
+49 201 18-34150
Email:

Curriculum Vitae:

Education

10/2015 - 04/2019: Business Information Systems, B. Sc., University of Duisburg-Essen

  • Bachelor Thesis: "Design Thinking: Theoretical Background, Key Concepts, and Application Potentials in the Context of Software Development"

04/2019 - 05/2021: Business Information Systems, M. Sc., University of Duisburg-Essen

  • Master Thesis: "Contingency Adaption Through Deep Learning: A Critical Reflection on the Applicability of Deep Learning for Organizational Problem-Solving"

Professional Positions

since 08/2021: Research Assistant (Wissenschaftlicher Mitarbeiter) at the Research Group for Information Systems and Enterprise Modelling, University of Duisburg-Essen

10/2019 - 07/2021: Student Assistant (Wissenschaftliche Hilfskraft) at the Research Group for Information Systems and Enterprise Modelling, University of Duisburg-Essen

03/2018 - 01/2020: Working Student in the area of Metadata Managament, Data Governance, and Data License Management at E.ON Digital Technology GmbH, Essen

07/2017 - 12/2017: Working Student in the area of Database Testing at Finanz-Informatik GmbH & Ko. KG, Münster

Publications:

Filter:
  • Frank, Ulrich; Maier, Pierre; Bock, Alexander: Low Code Platforms: Promises, Concepts and Prospects: A Comparative Study of Ten Systems - ICB Research Report, 70. Essen 2021. doi:10.17185/duepublico/75244) Full textCitationDetails
    Low Code Platforms: Promises, Concepts and Prospects: A Comparative Study of Ten Systems

    In recent years, the catchword “low‐code” has evolved into what can be seen as a major trend
    in software development platforms. A growing number of vendors respond to this trend by
    offering software development platforms that promise limited need for coding only and a tremendous
    boost in productivity. Both aspects have been the subject of intensive research over
    many years in areas such as domain‐specific modeling languages, model‐driven software development,
    or generative programming. Therefore, the obvious question is how ʺlow codeʺ
    platforms differ from such approaches and what specific performance features they offer.
    Since there is no unified definition of “low‐code”, the only way to develop an elaborate understanding
    of what it is – and might be – is to analyze the actual use of the term. For obvious
    reasons, it is not promising in this respect to rely on marketing announcements made by vendors.
    Instead, it seems more appropriate to examine “low‐code” platforms. This research report
    presents a study of 10 relevant platforms, capturing and assessing common characteristics
    as well as specific features of individual tools. The study is guided by a method that consists
    of a conceptual framework, which provides a uniform structure to describe and compare “lowcode”
    platforms, and a process model that describes the sequence of steps.

Tutored Theses:

  • Use of Machine Learning in the Manufacturing Industry: Design of a Method for the Identification and Selection of Application Possibilities (Master Thesis Business Information Systems, in progress)
  • Customer Churn Prediction in Literature and Practice - Design of a Process Model for the Selection of a Churn Prediction Model in Contractual Settings (Bachelor Thesis Business Information Systems, 2022)
  • Problem structuring methods in requirements engineering – Development of a suitability framework for use in requirements elicitation (Bachelor Thesis Business Administration, 2022)
  • Deep Learning in Organizations: Development and Application of an Evaluation Framework for the Comparison of Explainable Artificial Intelligence Methods (Bachelor Thesis Business Information Systems, 2022)