Mapping is the process of creating data element mappings between two distinct data models. Data mapping is often used as a first step for a wide variety of data integration tasks. One important aspect is the standardization of data, which offers many advantages:

• internal workflow processes are simplified
• innovative applications based on heterogeneous sources, e.g., mash-ups (new content is generated by combining already existing content)
• semantic differences are consolidated, allowing for the analysis and visualization of data

Mapping from many, often proprietary, different input schemas to one common, collection-specific schema is an everyday task. Knowledge of metadata standards like MARC and Dublin Core and the use of appropriate software tools are indispensable for these tasks. Target schemas are written in XML. XSLT and Java are used for data transformation. Special expertise exists for handling patents and chemistry documents.

What can KnowEsis Mapping Service do for you?

• Migrate existing collections to a well organized repository
• Analyze data formats
• Establish mapping rules
• Normalize and map to standard formats