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Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources ( XML DTDs and XML Schemata). Schema matching is recognized to be one of the basic operations required by the process of data integration [3], and thus has a great impact on its outcome. Schema mappings (the outcome of the matching process) can serve in tasks of generating global schemata, query rewriting over heterogeneous sources, duplicate data elimination, and automatic streamlining of workflow activities that involve heterogeneous data sources. As such, schema matching has impact on numerous applications. It impacts business, where company data sources continuously realign due to changing markets. It also impacts life sciences, where scientific workflows cross system boundaries more often than not.

Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources ( XML DTDs and XML Schemata). Schema matching is recognized to be one of the basic operations required by the process of data integration [3], and thus has a great impact on its outcome. Schema mappings (the outcome of the matching process) can serve in tasks of generating global schemata, query rewriting over heterogeneous sources, duplicate data elimination, and automatic streamlining of workflow activities that involve heterogeneous data sources. As such, schema matching has impact on numerous applications. It impacts business, where company data sources continuously realign due to changing markets. It also impacts life sciences, where scientific workflows cross system boundaries more often than not.

Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources ( XML DTDs and XML Schemata). Schema matching is recognized to be one of the basic operations required by the process of data integration [3], and thus has a great impact on its outcome. Schema mappings (the outcome of the matching process) can serve in tasks of generating global schemata, query rewriting over heterogeneous sources, duplicate data elimination, and automatic streamlining of workflow activities that involve heterogeneous data sources. As such, schema matching has impact on numerous applications. It impacts business, where company data sources continuously realign due to changing markets. It also impacts life sciences, where scientific workflows cross system boundaries more often than not.

Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources ( XML DTDs and XML Schemata). Schema matching is recognized to be one of the basic operations required by the process of data integration [3], and thus has a great impact on its outcome. Schema mappings (the outcome of the matching process) can serve in tasks of generating global schemata, query rewriting over heterogeneous sources, duplicate data elimination, and automatic streamlining of workflow activities that involve heterogeneous data sources. As such, schema matching has impact on numerous applications. It impacts business, where company data sources continuously realign due to changing markets. It also impacts life sciences, where scientific workflows cross system boundaries more often than not.

Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources ( XML DTDs and XML Schemata). Schema matching is recognized to be one of the basic operations required by the process of data integration [3], and thus has a great impact on its outcome. Schema mappings (the outcome of the matching process) can serve in tasks of generating global schemata, query rewriting over heterogeneous sources, duplicate data elimination, and automatic streamlining of workflow activities that involve heterogeneous data sources. As such, schema matching has impact on numerous applications. It impacts business, where company data sources continuously realign due to changing markets. It also impacts life sciences, where scientific workflows cross system boundaries more often than not.

Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources ( XML DTDs and XML Schemata). Schema matching is recognized to be one of the basic operations required by the process of data integration [3], and thus has a great impact on its outcome. Schema mappings (the outcome of the matching process) can serve in tasks of generating global schemata, query rewriting over heterogeneous sources, duplicate data elimination, and automatic streamlining of workflow activities that involve heterogeneous data sources. As such, schema matching has impact on numerous applications. It impacts business, where company data sources continuously realign due to changing markets. It also impacts life sciences, where scientific workflows cross system boundaries more often than not.

Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources ( XML DTDs and XML Schemata). Schema matching is recognized to be one of the basic operations required by the process of data integration [3], and thus has a great impact on its outcome. Schema mappings (the outcome of the matching process) can serve in tasks of generating global schemata, query rewriting over heterogeneous sources, duplicate data elimination, and automatic streamlining of workflow activities that involve heterogeneous data sources. As such, schema matching has impact on numerous applications. It impacts business, where company data sources continuously realign due to changing markets. It also impacts life sciences, where scientific workflows cross system boundaries more often than not.

Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources ( XML DTDs and XML Schemata). Schema matching is recognized to be one of the basic operations required by the process of data integration [3], and thus has a great impact on its outcome. Schema mappings (the outcome of the matching process) can serve in tasks of generating global schemata, query rewriting over heterogeneous sources, duplicate data elimination, and automatic streamlining of workflow activities that involve heterogeneous data sources. As such, schema matching has impact on numerous applications. It impacts business, where company data sources continuously realign due to changing markets. It also impacts life sciences, where scientific workflows cross system boundaries more often than not.

Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources ( XML DTDs and XML Schemata). Schema matching is recognized to be one of the basic operations required by the process of data integration [3], and thus has a great impact on its outcome. Schema mappings (the outcome of the matching process) can serve in tasks of generating global schemata, query rewriting over heterogeneous sources, duplicate data elimination, and automatic streamlining of workflow activities that involve heterogeneous data sources. As such, schema matching has impact on numerous applications. It impacts business, where company data sources continuously realign due to changing markets. It also impacts life sciences, where scientific workflows cross system boundaries more often than not.

Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources ( XML DTDs and XML Schemata). Schema matching is recognized to be one of the basic operations required by the process of data integration [3], and thus has a great impact on its outcome. Schema mappings (the outcome of the matching process) can serve in tasks of generating global schemata, query rewriting over heterogeneous sources, duplicate data elimination, and automatic streamlining of workflow activities that involve heterogeneous data sources. As such, schema matching has impact on numerous applications. It impacts business, where company data sources continuously realign due to changing markets. It also impacts life sciences, where scientific workflows cross system boundaries more often than not.