Data Science and Analytics for Decision Support (SIG DSA)

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Paper Type

Complete

Paper Number

1431

Description

For many years now big data presents itself as a technical starting point for a multitude of different trends, including data science, artificial intelligence, the internet of things, industry 4.0, and others. However, many decision makers are still challenged by the realization of related projects. One of the major problems arises from the multitude of existing technologies as well their selection and application. Due to that, in this paper, applying the design science research methodology, a comprehensive investigation about the application of multi criteria decision making (MCDM) methods is conducted. Apart from a structured literature review that seeks to identify relevant requirements for the technology selection, a conceptual model, as well as a prototypical implementation in the form of a decision support system, are presented. In the future, further MCDM approaches shall be tested and the system implemented in larger processes, including general sensemaking, system architecture modeling and rapid deployment.

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Aug 9th, 12:00 AM

Applying Multi-Criteria Decision-Making for the Selection of Big Data Technologies

For many years now big data presents itself as a technical starting point for a multitude of different trends, including data science, artificial intelligence, the internet of things, industry 4.0, and others. However, many decision makers are still challenged by the realization of related projects. One of the major problems arises from the multitude of existing technologies as well their selection and application. Due to that, in this paper, applying the design science research methodology, a comprehensive investigation about the application of multi criteria decision making (MCDM) methods is conducted. Apart from a structured literature review that seeks to identify relevant requirements for the technology selection, a conceptual model, as well as a prototypical implementation in the form of a decision support system, are presented. In the future, further MCDM approaches shall be tested and the system implemented in larger processes, including general sensemaking, system architecture modeling and rapid deployment.

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