Using Multilevel Business Artifacts for Knowledge Management in Analytics Projects

  • Simon Staudinger (Speaker)

Activity: Talk or presentationContributed talkscience-to-science

Description

Analytics projects often follow a generic process model, which maps out the main stages and tasks for conducting an analytics project while granting leeway to the project manager regarding the specific execution. A generic process model is instantiated by various organizations for projects applying different types of analytics - descriptive, predictive, prescriptive, etc. - on different use cases in various domains, using vastly different data. Each organization, each type of analytics, and each individual project thus requires a customized process tailored to the specific needs of the organization, type of analytics, and individual project. At each stage of a data analytics project, the project team has to assess the use case (analytics problem) and determine the course of action. Proper documentation of assessment and course of action, i.e., the design decisions and the underlying motivations, facilitates development in the subsequent stages and tasks as well as after deployment when using the developed system. In this paper, we present a use case for multilevel modeling, namely the documentation of knowledge related to analytics projects and data analyses, which are processes aimed at finding patterns in data. We employ the concept of multilevel business artifact, which allows for the representation of data and life cycle models in a single object at multiple levels of abstraction while granting the flexibility to specialize models in objects at lower levels. We use the real-world problem of flight delay prediction as a running example to illustrate the use of multilevel business artifacts for knowledge management in analytics projects.
Period02 Oct 2023
Event title10th International Workshop on Multi-Level Modeling (MULTI 2023) in conjunction with the 26th International Conference on Model Driven Engineering Languages and Systems (MODELS 2023), October 1-6, 2023, Västeras, Sweden
Event typeConference
LocationSwedenShow on map

Fields of science

  • 102028 Knowledge engineering
  • 102016 IT security
  • 102027 Web engineering
  • 503008 E-learning
  • 102 Computer Sciences
  • 502058 Digital transformation
  • 509026 Digitalisation research
  • 502050 Business informatics
  • 102030 Semantic technologies
  • 102033 Data mining
  • 102010 Database systems
  • 102035 Data science
  • 102015 Information systems
  • 102025 Distributed systems

JKU Focus areas

  • Digital Transformation