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Five-Level Multi-Application Schema Evolution* - ceur-ws

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The document discusses the concept of schema evolution in XML, emphasizing that it should be viewed from a multi-application perspective rather than just a single application. It introduces a five-level model for schema evolution, which includes extensional, operational, logical, platform-specific, and platform-independent levels. The authors argue that changes in one XML schema can affect others within the same problem domain and detail how transformations at each level can propagate to maintain consistency across applications. The paper also identifies gaps in existing research and outlines future work needed to implement their proposed system.

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What is meant by schema evolution?

What Is Schema Evolution? Schema evolution is a feature that allows users to easily change a tables current schema to accommodate data that is changing over time. Most commonly, its used when performing an append or overwrite operation, to automatically adapt the schema to include one or more new columns.

How do you cite Ceur?

The main elements of a CEUR-WS citation, as in any other citation style, are the author of the source, its title, and the year of publication. Depending on the source, its type (for example, if it is a video, patent or disc), place of publication, volume or issue number, pages, etc. can be specified.

What are the use cases of schema evolution?

The Most Important Schema Evolution Use Cases Big Data Processing: Schema Evolution allows for the modification of data structures in big data platforms to support changing analytical needs and accommodate evolving data sources.

What is schema evolution in hive?

Schema evolution allows you to update the schema used to write new data while maintaining backwards compatibility with the schemas of your old data. Then you can read it all together as if all of the data has one schema.

What are the best practices for schema evolution?

Best practices for schema evolution in big data: Maintain versioned schemas. Ensure backward compatibility. Implement data validation. Use Avro or Parquet for serialization.

How do you evolve schema?

Analysis: The first step in schema evolution is to analyze the existing schema and identify areas that need to be modified or updated. Design: The next step is to design the new schema, taking into account the new data elements or changes to existing ones.

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