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, ontologies, taxonomies), in many domains (e.g. libraries, life science, etc), Matching Techniques are called to overcome the challenge of aligning and reconciling these different interrelated representations. In this paper, we are interested in studying large scale matching approaches. We define a quality of Matching (QoM) that can be used to evaluate large scale Matching systems. We survey the techniques of large scale matching, when a large number of schemas/ontologies and attributes are involved. We attempt to cover a variety of techniques for schema matching called Pair-wise and Holistic, as well as a set of useful optimization techniques. One can acknowledge that this domain is on top of effervescence and large scale matching need much more advances. So, we propose a contribution that deals with the creation of a hybrid approach that combines these techniques.