With the progress of new technologies of information and communication, more and more producers of data exist. On the other hand, the web forms a huge support of all these kinds of data. Unfortunately, existing data is not proper due to the existence of the same information in different sources, as well as erroneous and incomplete data. The aim of data integration systems is to offer to a user a unique interface to query a number of sources. A key challenge of such systems is to deal with conflicting information from the same source or from different sources. We present, in this paper, the resolution of conflict at the instance level into two stages: references reconciliation and data fusion. The reference reconciliation methods seek to decide if two data descriptions are references to the same entity in reality. We define the principles of reconciliation method then we distinguish the methods of reference reconciliation, first on how to use the descriptions of references, then the way to acquire knowledge. We finish this section by discussing some current data reconciliation issues that are the subject of current research. Data fusion in turn, has the objective to merge duplicates into a single representation while resolving conflicts between the data. We define first the conflicts classification, the strategies for dealing with conflicts and the implementing conflict management strategies. We present then, the relational operators and data fusion techniques. Likewise, we finish this section by discussing some current data fusion issues that are the subject of current research.

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Providing automatic integration solutions is the key to the success of applications managing massive amounts of data. Two main problems stand out in the major studies: i the management of the source heterogeneity ii the reconciliation of query results.To tackle the first problem, formal ontologies are used to explicit the semantic of data. The reconciliation problem consists in deciding whether different identifiers refer to the same instance. Two main trends emerge in the reconciliation process:i the assumption that different source entities representing the same concept have the same key -a strong hypothesis that violates the autonomy of sources.ii The use of statistical methods that identify affinities between conceptsnot suitable for sensitive-applications.In this paper, we propose a methodology integrating sources referencing shared domain ontology enriched with functional dependencies (FD).The presence of FD gives more autonomy to sources when choosing their primary keys and allows deriving a reconciliation key for a given query. The methodology is then validated using LUBM.

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