Matching Techniques are becoming a very attractive research topic. With the development and the use of a large variety of data (e.g. DB schemas, XML schemas, ontologies), in many domains (e.g. semantic web, E-business, etc), matching techniques are called to overcome the challenge of aligning these different data. In this paper, we are interested in studying the quality of large scale matching systems. We define and propose a quality of Matching (QoM) that can be used to evaluate large scale matching systems. We survey the techniques, called optimization techniques, used in existing matching approaches to improve this quality. One can acknowledge that this domain is on top of effervescence and large scale matching need much more advances. So, we demonstrate how quality evaluation can be integrated in our scalable matching system PLASMA.