Data Envelopment Analysis (DEA) should be viewed as a method (or tool) for data-oriented analytics.
DEA is a data-oriented tool for performance evaluation & benchmarking. DEA generates a composite index. DEA identifies an envelopment of the data.
DEA is a classification tool. DEA measures efficiency & productivity.

Let's discover the new uses and potentials of DEA under
big data research.

Network DEA is based upon DEA ratios, but can behave very differently from the standard (or traditional) DEA. Network DEA may not be able to be converted into linear programming problems. Depending on the specific network structures of the decision making units (DMUs), network DEA can be solved via parametric linear programming and most recently by using second-order cone programming techniques.

There are many new research opportunities within the network DEA modeling. Convex optimization techniques need to be employed and adopted for solving non-convex network DEA mdoels.

Read a (draft) paper by Joe Zhu.

Note that the standard DEA models can be presented in either envelopment or multiplier form. However, under network DEA, such a duality no longer exists. In the dual to the multiplier DEA model (namely, the envelopment model), researchers discover the "convexity" and established a link between DEA and production function. This indicates that returns to scale (RTS) assumptions when applied to the network DEA need to be investigated.

Under the DEA ratio, the assumptions of constrant RTS (CRS) and variable RTS (VRS) represent two different shapes of the DEA best-practice frontier. Under the standard DEA, a CRS score cannot exceed the corresponding VRS score. However, under network DEA, this observarion may not hold.

although it bears with the name of "DEA".

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