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A Snapshot Survey of Data Acquisition Forms in Multi-Attribute Decision-Making Studies
Abstract
Multi-attribute decision-making (MADM) analysis is an important field widely used in the decision-making process. As the volume of data increases, it becomes critical to have a comprehensive understanding of large-scale data collection. However, current research lacks a holistic approach to obtaining large-scale data. This chapter aims to address this research gap by summarizing classic papers, open datasets, remote sensing data, sentiment analysis, and questionnaire survey data collection forms in MADM research. Classic papers provide a wealth of foundational knowledge, while open datasets provide diverse and large-scale data. Additionally, remote sensing data provides real-time information for urban planning and environmental management decisions. Finally, sentiment analysis leverages social media to gain unique insights, and questionnaire surveys are valid. Overall, this chapter helps researchers and professionals improve their selection and design of data collection methods to ensure reliable and impactful data collection, thereby improving the ability to make informed decisions.
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