Schema Evolution publication categorizer

There is a new generation of databases specifically addressing Big Data Variety: Multi-model databases store and process structurally heterogeneous data, managing several data models in one integrated backend. Yet one of the many challenges these systems face is evolution management. In our demonstration, we present our prototype implementation of a tool called MM-evolver. MM-evolver carries out user-triggered schema modification operations over a multi-model database, and propagates them across all models.

Recent position papers demand more schema flexibility, such as the ability to handle variational data [3, 42]. Many agile software developers have long since turned towards NoSQL database systems such as MongoDB 1, Couchbase 2, or ArangoDB 3 which are schema-flexible, or even altogether schema-free. They allow to store datasets in different structural versions to co-exist. Yet even when the database management system does not maintain an explicit schema, there is commonly an implicit schema, as the application code makes assumptions about the structure of the stored data.

During the development of NoSQL-backed software, the database schema evolves naturally alongside the application code. Especially in agile development, new application releases are deployed frequently. Eventually, decisions have to be made regarding the migration of versioned legacy data which is persisted in the cloud-hosted production database.

Supporting legacy applications when the database schema evolves represents a long-standing challenge of practical and theoretical importance. Recent work has produced algorithms and systems that automate the process of data migration and query adaptation; how- ever, the problems of evolving integrity constraints and supporting legacy updates under schema and integrity constraints evolution are significantly more difficult and have thus far remained unsolved.

The problem of archiving and querying the history of a database is made more complex by the fact that, along with the database content, the database schema also evolves with time.

The complexity, cost, and down-time currently created by the database schema evolution process is the source of incessant problems in the life of information systems and a major stumbling block that prevent graceful upgrades. Furthermore, our studies shows that the serious problems encountered by traditional information systems are now further exacerbated in web information systems and cooperative scientific databases where the frequency of schema changes has increased while tolerance for downtimes has nearly disappeared.