AG Netzbasierte Informationssysteme (Networked Information Systems) | Freie Universitšt Berlin

MOMA Framework

FU Berlin

MOMA-M

To allow a manual selection of matching approaches (MOMA-M) within the MOMA Framework, the Analytic Hierarchy Process (AHP), providing a mathematically rigorous application and proven process for prioritization and decision-making, is to be applied.

The Analytic Hierarchy Process (AHP) allows the gathering of knowledge about a particular problem, the quantification of subjective opinions as well to forcing the comparison of alternatives in relation to established criteria and had been already successfully applied for different selections especially for reuse decisions.

The AHP-method consists of the following steps:

  1. definition of a problem or project objectives;
  2. building a decision hierarchy: AHP provides a means to break down the problem into a hierarchy of subproblems (hierarchy built on the goal, criteria, sub-criteria and alternatives), which can be more easily comprehended and subjectively evaluated
  3. data collection: data is collected from domain experts corresponding to the hierarchical structure in the pairwise comparison of the alternatives on a qualitative scale; this step assesses the characteristics of each alternative;
  4. building a pairwise comparison: for each level of criteria (sub-criteria and criteria) pairwise comparisons between sibling nodes4 is to be built and organized into a square matrix;
  5. calculation of a final result: the ratings of each alternative (cf. step 3) is multiplied by the weight of the sub-criteria (cf. step 4) and aggregated to obtain local ratings with respect to each criterion.
By reducing complex decisions (which matching is suitable) to a series of pair-wise comparisons and synthesizing the results (list of suitable algorithms), decision makers arrive at the best decision based on a clear rationale. By reducing complex decisions (which matching is suitable) to a series of pair-wise comparisons and synthesizing the results (list of suitable algorithms), decision makers arrive at the best decision based on a clear rationale. By reducing complex decisions (which matching is suitable) to a series of pair-wise comparisons and synthesizing the results (list of suitable algorithms), decision makers arrive at the best decision based on a clear rationale.

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