Evaluating MCDM Methodologies for Data Integration Tool Selection: A Comparative Study of COPRAS and NR-TOPSI

In the era of big data, selecting the right data integration tool is crucial for organizations that wish to harness the full potential of the vast amount of data. This research explores the application of Multi-Criteria Decision-Making (MCDM) framework, specifically COPRAS and NR-TOPSIS, to guide the selection process of data integration tools. Based on data collected from mobile crowd computing resources, the research conducts a series of experiments to evaluate the consistency and stability of the selected MCDM methods across a variety of decision-making contexts. Findings indicate that COPRAS, in conjunction with the entropy weighting method, provides a more stable, intuitive, and accurate framework compared to NR-TOPSIS, especially in rank coherence, sensitivity to criteria weight changes, and rank reversal scenarios. The research validates the efficacy of combining COPRAS and the entropy method in offering a systematic and data-driven approach for technology selection tailored to accommodate the dynamic preferences of decision-makers and the evolving landscape of data integration solutions. By advancing the application of MCDM methodologies within the big data domain, this research equips data professionals and enterprises with a robust decision-support tool, fostering informed choices that enhance operational efficiency and leverage data as a strategic asset.

Author

• Huang, Zi Han

Language

• English

Keyword

• Data Integration
• COPRAS
• MCDM
• TOPSIS
• Entropy
• Big Data

Date created

• 2024

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Sarkani, Shahram
• Mazzuchi, Thomas A

Committee Member(s)

• Etemadi, Amir
• Blackburn, Timothy D

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/ns0646752