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This paper is a review paper focusing on the methodological development of Data Envelopment Analysis (DEA), a multi-factor performance measurement and improvement tool. Since its introduction in 1978, vast studies have been done on DEA, causing significant growth in its methodology and applications in the real world. The purpose of this paper is to provide a general introduction to DEA. The basic DEA models and some important methodological extensions of DEA, such as multilevel DEA models, stochastic DEA models, and fuzzy DEA models, are discussed in the paper. In addition, some current and future research trends are highlighted.