This method employs a
central broker to align the various formats and schemas of the aggregated information.
The central hub approach is suited for transaction processing and meta-data alignment
across multiple domains. For example, the Customer Data Integration (CDI)18 architecture
represents this idea. It employs a central aggregating broker that is responsible for
retrieving, cleansing, and aligning data formats, and maintaining a single view of customer
critical data. (2) The second approach is the analytical aggregation model, by
which a 360-degree view of enterprise data is collected for analysis and management
reporting purposes. This holistic data gathering spans multiple distributed services in the
enterprise, enables database schema reconciliation, and broadens the data aggregation
paradigm beyond silo organizations. For example, the Entity Aggregation (EI) for Business
Intelligence19 method enables mapping between database schemas and argues for
the reconciliation of data formats.
??? Validation. The conceptual data machine also depicts data validation solutions. This
technological requirement is typically satisfied when data is confirmed to be intact and its
format complies with organizational schema validation standards. Currently, the serviceoriented
industry supports XML/SOA data validation that is enforced by XML schemas.
Data validation requirements should also include message validation for exchanged XML
documents.
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