Semantic heterogeneity occurs when different databases, often built independently within the same domain, represent or interpret the same data in different ways. This creates problems when trying to integrate or query such databases together, especially in distributed or federated database systems (FDBSs).
Why Semantic Heterogeneity Occurs
It mainly arises due to design autonomy where each database has the freedom to define:
- Universe of Discourse: For example, customer databases in the US and China might store different sets of attributes for the same entity due to local accounting rules or currencies.
- Representation and Naming: Same data may be stored under different names (e.g., Customer_ID vs ClientNumber) or in different formats (e.g., date or currency formats).
- Interpretation of Data: A value like "priority customer" might be defined differently across systems.
- Transaction Rules and Policies: Local databases may have their own constraints, serializability rules, or compensating transactions.
- Derived Data & Summaries: Some databases might include computed fields or summaries which others do not.
Challenges Caused by Semantic Heterogeneity
- Data integration: Semantic heterogeneity can make it difficult to integrate data from multiple databases into a single system. This can result in data duplication, data inconsistencies, and reduced data quality.
- Query processing: Semantic heterogeneity can make it difficult to process queries that involve data from multiple databases. This can result in slow query performance and increased processing time.
- Application development: Semantic heterogeneity can make it difficult to develop applications that rely on data from multiple databases. This can result in increased development time and reduced application functionality.
Solutions to Manage Semantic Heterogeneity
- Data Mapping: Map equivalent fields and values across different schemas.
- Data Transformation: Convert data formats and structures during integration.
- Semantic Reconciliation: Align the intended meaning of data fields across databases.
- Metadata Management: Use metadata to document how data is structured and what it means.